text:
, allowing for rapid identification, navigation, and modification of the small, disconnected erroneous labels within an atlas. The technical details for the implementation and performance of LabelAtlasEditor are demonstrated using an application of improving a set of 20 Huntingtons Disease-specific multi-modal brain atlases. Additionally, we present the advantages and limitations of automatic atlas correction. After the correction of atlas inconsistencies and small, disconnected regions, the number of unidentified voxels for each dataset was reduced on average by 68.48{\%}.},
annote = {From Duplicate 1 (An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases. - Forbes, Jessica L; Kim, Regina E Y; Paulsen, Jane S; Johnson, Hans J)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper. I had contributions to the software methods development, interpretation of validation results for this work. I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process. :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases. - Forbes, Jessica L; Kim, Regina EY Y; Paulsen, Jane S.; Johnson, Hans J.)

From Duplicate 1 (An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases. - Forbes, Jessica L; Kim, Regina EY Y; Paulsen, Jane S.; Johnson, Hans J.)

From Duplicate 1 (An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases. - Forbes, Jessica L; Kim, Regina E Y; Paulsen, Jane S; Johnson, Hans J)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper. I had contributions to the software methods development, interpretation of validation results for this work. I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process. :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases. - Forbes, Jessica L; Kim, Regina EY; Paulsen, Jane S.; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases. - Forbes, Jessica L; Kim, Regina EY Y; Paulsen, Jane S.; Johnson, Hans J.)

From Duplicate 1 (An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases. - Forbes, Jessica L; Kim, Regina EY Y; Paulsen, Jane S.; Johnson, Hans J.)

From Duplicate 1 (An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases. - Forbes, Jessica L; Kim, Regina E Y; Paulsen, Jane S; Johnson, Hans J)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper. I had contributions to the software methods development, interpretation of validation results for this work. I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process. :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases. - Forbes, Jessica L; Kim, Regina EY; Paulsen, Jane S.; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases. - Forbes, Jessica L; Kim, Regina E Y; Paulsen, Jane S; Johnson, Hans J)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper. I had contributions to the software methods development, interpretation of validation results for this work. I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process. :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}},
author = {Forbes, Jessica L. and Kim, Regina E.Y. and Paulsen, Jane S. and Johnson, Hans J.},
doi = {10.3389/fninf.2016.00029},
file = {:Users/johnsonhj/Library/Application Support/Mendeley Desktop/Downloaded/Forbes et al. - 2016 - An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases.pdf:pdf},
issn = {16625196},
journal = {Frontiers in Neuroinformatics},
keywords = {Brain MRI,Huntingtons Disease,ITK,Label atlas,Multi-atlas,Multi-modal,Open-source},
month = {aug},
number = {AUG},
pages = {1--11},
pmid = {27536233},
publisher = {Frontiers Media SA},
title = {{An open-source label atlas correction tool and preliminary results on huntingtons disease whole-brain MRI atlases}},
url = {http://journal.frontiersin.org/Article/10.3389/fninf.2016.00029/abstract{\%}5Cnhttp://www.ncbi.nlm.nih.gov/pubmed/27536233{\%}5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4971025},
volume = {10},
year = {2016}
}
@article{RID:1020151228438-28,
abstract = {The BRAINS (Brain Research: Analysis of Images, Networks, and Systems) image analysis software has been in use, and in constant development, for over 20. years. The original neuroimage analysis pipeline using BRAINS was designed as a semiautomated procedure to measure volumes of the cerebral lobes and subcortical structures, requiring manual intervention at several stages in the process. Through use of advanced image processing algorithms the need for manual intervention at stages of image realignment, tissue sampling, and mask editing have been eliminated. In addition, inhomogeneity correction, intensity normalization, and mask cleaning routines have been added to improve the accuracy and consistency of the results. The fully automated method, AutoWorkup, is shown in this study to be more reliable (ICC ≥ 0.96, Jaccard index ≥ 0.80, and Dice index ≥ 0.89 for all tissues in all regions) than the average of 18 manual raters. On a set of 1130 good quality scans, the failure rate for correct realignment was 1.1{\%}, and manual editing of the brain mask was required on 4{\%} of the scans. In other tests, AutoWorkup is shown to produce measures that are reliable for data acquired across scanners, scanner vendors, and across sequences. Application of AutoWorkup for the analysis of data from the 32-site, multivendor PREDICT-HD study yield estimates of reliability to be greater than or equal to 0.90 for all tissues and regions. {\textcopyright} 2010 Elsevier Inc.},
annote = {From Duplicate 1 (Fully automated analysis using BRAINS: AutoWorkup - Pierson, Ronald K.; Johnson, Hans J.; Harris, Gregory; Keefe, Helen; Paulsen, Jane S.; Andreasen, Nancy C.; Magnotta, Vincent A.)

{\#}{\#}CONTRIBUTIONS: I developed custom analysis software to achieve the desired interpretation of results.   I had substantial contributions to the software methods development, interpretation of validation results for this work.  I assisted with critically reviewing and revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Fully automated analysis using BRAINS: AutoWorkup - Pierson, Ronald K.; Johnson, Hans J.; Harris, Gregory; Keefe, Helen; Paulsen, Jane S.; Andreasen, Nancy C.; Magnotta, Vincent A.)

From Duplicate 2 (Fully automated analysis using BRAINS: AutoWorkup - Pierson, Ronald K.; Johnson, Hans J.; Harris, Gregory; Keefe, Helen; Paulsen, Jane S.; Andreasen, Nancy C.; Magnotta, Vincent A.)

From Duplicate 1 (Fully automated analysis using BRAINS: AutoWorkup - Pierson, Ronald K.; Johnson, Hans J.; Harris, Gregory; Keefe, Helen; Paulsen, Jane S.; Andreasen, Nancy C.; Magnotta, Vincent A.)

From Duplicate 1 (Fully automated analysis using BRAINS: AutoWorkup - Pierson, Ronald K.; Johnson, Hans J.; Harris, Gregory; Keefe, Helen; Paulsen, Jane S.; Andreasen, Nancy C.; Magnotta, Vincent A.)

{\#}{\#}CONTRIBUTIONS: I developed custom analysis software to achieve the desired interpretation of results.   I had substantial contributions to the software methods development, interpretation of validation results for this work.  I assisted with critically reviewing and revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}},
author = {Pierson, Ronald and Johnson, Hans and Harris, Gregory and Keefe, Helen and Paulsen, Jane S. and Andreasen, Nancy C. and Magnotta, Vincent A.},
doi = {10.1016/j.neuroimage.2010.06.047},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Pierson et al/NeuroImage/Pierson et al. - 2011 - Fully automated analysis using BRAINS AutoWorkup.pdf:pdf},
isbn = {1095-9572 (Electronic)$\backslash$n1053-8119 (Linking)},
issn = {10538119},
journal = {NeuroImage},
keywords = {Automated image analysis,BRAINS,Morphometry,Pipeline,Segmentation,Volumetric analysis},
month = {jan},
number = {1},
pages = {328--336},
pmid = {20600977},
publisher = {Elsevier Inc.},
title = {{Fully automated analysis using BRAINS: AutoWorkup}},
url = {http://www.sciencedirect.com/science/article/pii/S1053811910009055 papers2://publication/uuid/35F9A8B8-DD4A-4857-903F-16D3F7CCAEF7 http://www.ncbi.nlm.nih.gov/pubmed/20600977},
volume = {54},
year = {2011}
}
@inproceedings{Gerard2016,
abstract = {Lung segmentation is a critical initial step in planning radiation therapy interventions for lung cancer patients. Achieving robust automatic segmentation of lungs with large tumors is challenging due to large variations in lung morphology, tumor location, and tumor shape between subjects. We present an automatic method to segment lungs with large tumors in CT images using an initial intensity based segmentation followed by alpha shape construction and graph search. We evaluated our method by comparing automated segmentations to manual segmentations on twelve subjects. Computed metrics for segmentation quality include average surface distance of 0.727 mm and average DICE coefficient of 0.970. These results demonstrate that the proposed method accurately segments the entire lung regions both free of and in the presence of large tumors.},
annote = {From Duplicate 1 (Alpha Shapes for Lung Segmentation in the Presence of Large Tumors - Gerard, Sarah E; Johnson, Hans J.; Bayouth, John E; Christensen, Gary E.; Du, Kaifang; Guo, Junfeng; Reinhardt, Joseph M)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper. I was responsible for the data processing for one of the evaluated algorithms. I had the primary contributions to the interpretation of validation results for this work.  I contributed to revising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Alpha Shapes for Lung Segmentation in the Presence of Large Tumors - Gerard, Sarah E; Johnson, Hans J.; Bayouth, John E; Christensen, Gary E.; Du, Kaifang; Guo, Junfeng; Reinhardt, Joseph M)

From Duplicate 1 (Alpha Shapes for Lung Segmentation in the Presence of Large Tumors - Gerard, Sarah E; Johnson, Hans J.; Bayouth, John E; Christensen, Gary E.; Du, Kaifang; Guo, Junfeng; Reinhardt, Joseph M)

From Duplicate 1 (Alpha Shapes for Lung Segmentation in the Presence of Large Tumors - Gerard, Sarah E; Johnson, Hans J.; Bayouth, John E; Christensen, Gary E.; Du, Kaifang; Guo, Junfeng; Reinhardt, Joseph M)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper. I was responsible for the data processing for one of the evaluated algorithms. I had the primary contributions to the interpretation of validation results for this work.  I contributed to revising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Alpha Shapes for Lung Segmentation in the Presence of Large Tumors - Gerard, Sarah E; Johnson, Hans J; Bayouth, John E; Christensen, Gary E; Du, Kaifang; Guo, Junfeng; Reinhardt, Joseph M)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper. I was responsible for the data processing for one of the evaluated algorithms. I had the primary contributions to the interpretation of validation results for this work. I contributed to revising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Alpha Shapes for Lung Segmentation in the Presence of Large Tumors - Gerard, Sarah E; Johnson, Hans J.; Bayouth, John E; Christensen, Gary E.; Du, Kaifang; Guo, Junfeng; Reinhardt, Joseph M)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper. I was responsible for the data processing for one of the evaluated algorithms. I had the primary contributions to the interpretation of validation results for this work.  I contributed to revising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}},
author = {Gerard, Sarah E and Johnson, Hans J. and Bayouth, John E and Christensen, Gary E. and Du, Kaifang and Guo, Junfeng and Reinhardt, Joseph M},
booktitle = {PIA: Pulmonary Image Analysis},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Gerard et al/PIA Pulmonary Image Analysis/Gerard et al. - 2016 - Alpha Shapes for Lung Segmentation in the Presence of Large Tumors.pdf:pdf},
pages = {1--9},
title = {{Alpha Shapes for Lung Segmentation in the Presence of Large Tumors}},
year = {2016}
}
@article{avants2015neuroinformatics,
annote = {From Duplicate 1 (Neuroinformatics and the The Insight ToolKit - Avants, Brian B.; Johnson, Hans J.; Tustison, Nicholas J.)

From Duplicate 1 (Neuroinformatics and the The Insight ToolKit - Avants, Brian B.; Johnson, Hans J.; Tustison, Nicholas J.)

{\#}{\#}CONTRIBUTIONS: As a contributing guest editor for a special issue of "Frontiers in Neruoinformatics" I had substantial oversight in the criteria for selection, review and ultimate publication of materials in the issue.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Editorial :{\#}{\#}

From Duplicate 2 (Neuroinformatics and the The Insight ToolKit - Avants, Brian B.; Johnson, Hans J.; Tustison, Nicholas J.)

From Duplicate 2 (Neuroinformatics and the The Insight ToolKit - Avants, Brian B.; Johnson, Hans J.; Tustison, Nicholas J.)

{\#}{\#}CONTRIBUTIONS: As a contributing guest editor for a special issue of "Frontiers in Neruoinformatics" I had substantial oversight in the criteria for selection, review and ultimate publication of materials in the issue.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Editorial :{\#}{\#}},
author = {Avants, Brian B. and Johnson, Hans J. and Tustison, Nicholas J.},
doi = {10.3389/fninf.2015.00005},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Avants, Johnson, Tustison/Frontiers in neuroinformatics/Avants, Johnson, Tustison - 2015 - Neuroinformatics and the The Insight ToolKit.pdf:pdf},
issn = {16625196},
journal = {Frontiers in Neuroinformatics},
keywords = {C++,ITK,Open source,Registration,Segmentation},
number = {MAR},
pages = {1--3},
pmid = {25859213},
publisher = {Frontiers Media SA},
title = {{Neuroinformatics and the the insight toolkit}},
url = {http://www.frontiersin.org/Neuroinformatics/10.3389/fninf.2015.00005/full},
volume = {9},
year = {2015}
}
@article{oguz2014dtiprep,
abstract = {In the last decade, diffusion MRI (dMRI) studies of the human and animal brain have been used to investigate a multitude of pathologies and drug-related effects in neuroscience research. Study after study identifies white matter (WM) degeneration as a crucial biomarker for all these diseases. The tool of choice for studying WM is dMRI. However, dMRI has inherently low signal-to-noise ratio and its acquisition requires a relatively long scan time; in fact, the high loads required occasionally stress scanner hardware past the point of physical failure. As a result, many types of artifacts implicate the quality of diffusion imagery. Using these complex scans containing artifacts without quality control (QC) can result in considerable error and bias in the subsequent analysis, negatively affecting the results of research studies using them. However, dMRI QC remains an under-recognized issue in the dMRI community as there are no user-friendly tools commonly available to comprehensively address the issue of dMRI QC. As a result, current dMRI studies often perform a poor job at dMRI QC. Thorough QC of dMRI will reduce measurement noise and improve reproducibility, and sensitivity in neuroimaging studies; this will allow researchers to more fully exploit the power of the dMRI technique and will ultimately advance neuroscience. Therefore, in this manuscript, we present our open-source software, DTIPrep, as a unified, user friendly platform for thorough QC of dMRI data. These include artifacts caused by eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, as well as slice-wise and gradient-wise intensity inconsistencies. This paper summarizes a basic set of features of DTIPrep described earlier and focuses on newly added capabilities related to directional artifacts and bias analysis. {\textcopyright} 2014 Oguz, Farzinfar, Matsui, Budin, Liu, Gerig, Johnson and Styner.},
annote = {From Duplicate 1 (DTIPrep: quality control of diffusion-weighted images - Oguz, Ipek; Farzinfar, Mahshid; Matsui, Joy T.; Budin, Francois; Liu, Zhexing; Gerig, Guido; Johnson, Hans J.; Styner, Martin)

From Duplicate 1 (DTIPrep: quality control of diffusion-weighted images - Oguz, Ipek; Farzinfar, Mahshid; Matsui, Joy T.; Budin, Francois; Liu, Zhexing; Gerig, Guido; Johnson, Hans J.; Styner, Martin)

{\#}{\#}CONTRIBUTIONS: As a member of the National Alliance for Medical Imaging Computing (NAMIC) I collaborated on many software engineering projets.   I had substantial contributions to the software methods development, interpretation of validation results for this work.  I assisted with critically reviewing and revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (DTIPrep: quality control of diffusion-weighted images - Oguz, Ipek; Farzinfar, Mahshid; Matsui, Joy T.; Budin, Francois; Liu, Zhexing; Gerig, Guido; Johnson, Hans J.; Styner, Martin)

From Duplicate 1 (DTIPrep: quality control of diffusion-weighted images - Oguz, Ipek; Farzinfar, Mahshid; Matsui, Joy T.; Budin, Francois; Liu, Zhexing; Gerig, Guido; Johnson, Hans J.; Styner, Martin)

From Duplicate 2 (DTIPrep: quality control of diffusion-weighted images - Oguz, Ipek; Farzinfar, Mahshid; Matsui, Joy T.; Budin, Francois; Liu, Zhexing; Gerig, Guido; Johnson, Hans J.; Styner, Martin)

{\#}{\#}CONTRIBUTIONS: As a member of the National Alliance for Medical Imaging Computing (NAMIC) I collaborated on many software engineering projets.   I had substantial contributions to the software methods development, interpretation of validation results for this work.  I assisted with critically reviewing and revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (DTIPrep: quality control of diffusion-weighted images - Oguz, Ipek; Farzinfar, Mahshid; Matsui, Joy T.; Budin, Francois; Liu, Zhexing; Gerig, Guido; Johnson, Hans J.; Styner, Martin)

From Duplicate 1 (DTIPrep: quality control of diffusion-weighted images - Oguz, Ipek; Farzinfar, Mahshid; Matsui, Joy T.; Budin, Francois; Liu, Zhexing; Gerig, Guido; Johnson, Hans J.; Styner, Martin)

From Duplicate 2 (DTIPrep: quality control of diffusion-weighted images - Oguz, Ipek; Farzinfar, Mahshid; Matsui, Joy T.; Budin, Francois; Liu, Zhexing; Gerig, Guido; Johnson, Hans J.; Styner, Martin)

{\#}{\#}CONTRIBUTIONS: As a member of the National Alliance for Medical Imaging Computing (NAMIC) I collaborated on many software engineering projets.   I had substantial contributions to the software methods development, interpretation of validation results for this work.  I assisted with critically reviewing and revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}},
author = {Oguz, Ipek and Farzinfar, Mahshid and Matsui, Joy and Budin, Francois and Liu, Zhexing and Gerig, Guido and Johnson, Hans J. and Styner, Martin},
doi = {10.3389/fninf.2014.00004},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Oguz et al/Frontiers in neuroinformatics/Oguz et al. - 2014 - DTIPrep quality control of diffusion-weighted images.pdf:pdf},
isbn = {1662-5196 (Electronic)$\backslash$r1662-5196 (Linking)},
issn = {16625196},
journal = {Frontiers in Neuroinformatics},
keywords = {Diffusion MRI,Diffusion tensor imaging,Open-source,Preprocessing,Quality control,Software},
number = {JAN},
pages = {4},
pmid = {24523693},
publisher = {Frontiers Media SA},
title = {{DTIPrep: Quality control of diffusion-weighted images}},
url = {http://journal.frontiersin.org/article/10.3389/fninf.2014.00004/abstract{\%}0Ahttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3906573{\&}tool=pmcentrez{\&}rendertype=abstract{\%}5Cnhttp://journal.frontiersin.org/Journal/10.3389/fninf.2014.00004/full http://},
volume = {8},
year = {2014}
}
@techreport{Kim2011,
abstract = {This document describes an affine transformation algorithm as an additional feature for landmark based registration in ITK www.itk.org. The algorithm is based on the paper by Sp{\"{a}}th, H [2]. The author derives a set of linear equations from paired landmarks and generates an affine transform from them. The method implemented here gives more freedom in the choice of registration and/or initialization method in ITK. The submission describes ITK implementation of the algorithm.},
annote = {From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina EY Y; Johnson, Hans J.; Williams, Norman K; Kim, Eun Young Regina; Kim EYR Johnson HJ, Williams N K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina E Y; Johnson, Hans J; Williams, Norman K; Kim, Eun Young Regina; Kim EYR Johnson HJ, Williams N K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K; Kim EYR Johnson HJ, Williams N K)

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K)

From Duplicate 3 ( Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young; Johnson, Hans J.; Williams, Norman K )

From Duplicate 3 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina E Y; Johnson, Hans J.; Williams, Norman K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K; Kim EYR Johnson HJ, Williams N K)

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K)

From Duplicate 3 ( Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young; Johnson, Hans J.; Williams, Norman K )

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina EY; Johnson, Hans J.; Williams, Norman K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K; Kim EYR Johnson HJ, Williams N K)

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K)

From Duplicate 3 ( Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young; Johnson, Hans J.; Williams, Norman K )

From Duplicate 3 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina E Y; Johnson, Hans J.; Williams, Norman K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K; Kim EYR Johnson HJ, Williams N K)

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K)

From Duplicate 3 ( Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young; Johnson, Hans J.; Williams, Norman K )

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina E Y; Johnson, Hans J; Williams, Norman K; Kim, Eun Young Regina; Kim EYR Johnson HJ, Williams N K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K; Kim EYR Johnson HJ, Williams N K)

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K)

From Duplicate 3 ( Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young; Johnson, Hans J.; Williams, Norman K )

From Duplicate 3 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina E Y; Johnson, Hans J.; Williams, Norman K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K; Kim EYR Johnson HJ, Williams N K)

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K)

From Duplicate 3 ( Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young; Johnson, Hans J.; Williams, Norman K )},
author = {Kim, Regina EY Y and Johnson, Hans J. and Williams, Norman K and Kim, Eun Young Regina and {Kim EYR Johnson HJ}, Williams N K},
booktitle = {Engineering},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Kim et al/Engineering/Kim et al. - 2011 - Affine Transformation for Landmark Based Registration Initializer in ITK.pdf:pdf},
keywords = {Affine Transformation for Landmark Based Registrat,Insight Journal},
mendeley-tags = {Insight Journal},
pages = {1--8},
title = {{Affine Transformation for Landmark Based Registration Initializer in ITK}},
year = {2011}
}
@article{yaniv2018simpleitk,
abstract = {Modern scientific endeavors increasingly require team collaborations to construct and interpret complex computational workflows. This work describes an image-analysis environment that supports the use of computational tools that facilitate reproducible research and support scientists with varying levels of software development skills. The Jupyter notebook web application is the basis of an environment that enables flexible, well-documented, and reproducible workflows via literate programming. Image-analysis software development is made accessible to scientists with varying levels of programming experience via the use of the SimpleITK toolkit, a simplified interface to the Insight Segmentation and Registration Toolkit. Additional features of the development environment include user friendly data sharing using online data repositories and a testing framework that facilitates code maintenance. SimpleITK provides a large number of examples illustrating educational and research-oriented image analysis workflows for free download from GitHub under an Apache 2.0 license: github.com/InsightSoftwareConsortium/SimpleITK-Notebooks.},
author = {Yaniv, Ziv and Lowekamp, Bradley C. and Johnson, Hans J. and Beare, Richard},
doi = {10.1007/s10278-017-0037-8},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Yaniv et al/Journal of Digital Imaging/Yaniv et al. - 2018 - SimpleITK Image-Analysis Notebooks a Collaborative Environment for Education and Reproducible Research.pdf:pdf},
issn = {1618727X},
journal = {Journal of Digital Imaging},
keywords = {Image analysis,Open-source software,Python,R,Registration,Segmentation},
number = {3},
pages = {290--303},
pmid = {29181613},
publisher = {Springer International Publishing},
title = {{SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research}},
volume = {31},
year = {2018}
}
@inproceedings{shao2016population,
abstract = {This paper examines the shape collapse problem that occurs when registering a pair of images or a population of images of the brain to a reference (target) image coordinate system using diffeomorphic image registration. Shape collapse occurs when a foreground or background structure in an image with non-zero volume is transformed into a set of zero or near zero volume as measured on a discrete voxel lattice in the target image coordinate system. Shape collapse may occur during image registration when the moving image has a structure that is either missing or does not sufficiently overlap the corresponding structure in the target image[4]. Such a problem is common in image registration algorithms with large degrees of freedom such as many diffeomorphic image registration algorithms. Shape collapse is a concern when mapping functional data. For example, loss of signal may occur when mapping functional data such as fMRI, PET, SPECT using a transformation with a shape collapse if the functional signal occurs at the collapse region. This paper proposes an novel shape collapse measurement algorithm to detect the regions of shape collapse after image registration in pairwise registration. We further compute the shape collapse for a population of pairwise transformations such as occurs when registering many images to a common atlas coordinate system. Experiments are presented using the SyN diffeomorphic image registration algorithm. We demonstrate how changing the input parameters to the SyN registration algorithm can mitigate some of the collapse image registration artifacts.},
annote = {From Duplicate 1 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E.; Johnson, Hans J.; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P.; Shaffer, Joseph J.; Magnotta, Vincent A.; Fiedorowicz, Jess G.; Wemmie, John A)

From Duplicate 1 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E.; Johnson, Hans J.; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P.; Shaffer, Joseph J.; Magnotta, Vincent A.; Fiedorowicz, Jess G.; Wemmie, John A)

From Duplicate 1 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E; Johnson, Hans J; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P; Shaffer, Joseph J; Magnotta, Vincent A; Fiedorowicz, Jess G; Wemmie, John A)

From Duplicate 1 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E; Johnson, Hans J; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P; Shaffer, Joseph J; Magnotta, Vincent A; Fiedorowicz, Jess G; Wemmie, John A)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper. I had the primary contributions to the interpretation of validation results for this work. I contributed torevising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E.; Johnson, Hans J.; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P.; Shaffer, Joseph J.; Magnotta, Vincent A.; Fiedorowicz, Jess G.; Wemmie, John A)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper.  I had the primary contributions to the interpretation of validation results for this work.  I contributed torevising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E.; Johnson, Hans J.; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P.; Shaffer, Joseph J.; Magnotta, Vincent A.; Fiedorowicz, Jess G.; Wemmie, John A)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper.  I had the primary contributions to the interpretation of validation results for this work.  I contributed torevising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E.; Johnson, Hans J.; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P.; Shaffer, Joseph J.; Magnotta, Vincent A.; Fiedorowicz, Jess G.; Wemmie, John A)

From Duplicate 1 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E.; Johnson, Hans J.; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P.; Shaffer, Joseph J.; Magnotta, Vincent A.; Fiedorowicz, Jess G.; Wemmie, John A)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper.  I had the primary contributions to the interpretation of validation results for this work.  I contributed torevising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E.; Johnson, Hans J.; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P.; Shaffer, Joseph J.; Magnotta, Vincent A.; Fiedorowicz, Jess G.; Wemmie, John A)

From Duplicate 1 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E.; Johnson, Hans J.; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P.; Shaffer, Joseph J.; Magnotta, Vincent A.; Fiedorowicz, Jess G.; Wemmie, John A)

From Duplicate 1 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E; Johnson, Hans J; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P; Shaffer, Joseph J; Magnotta, Vincent A; Fiedorowicz, Jess G; Wemmie, John A)

From Duplicate 1 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E; Johnson, Hans J; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P; Shaffer, Joseph J; Magnotta, Vincent A; Fiedorowicz, Jess G; Wemmie, John A)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper. I had the primary contributions to the interpretation of validation results for this work. I contributed torevising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E.; Johnson, Hans J.; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P.; Shaffer, Joseph J.; Magnotta, Vincent A.; Fiedorowicz, Jess G.; Wemmie, John A)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper.  I had the primary contributions to the interpretation of validation results for this work.  I contributed torevising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E.; Johnson, Hans J.; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P.; Shaffer, Joseph J.; Magnotta, Vincent A.; Fiedorowicz, Jess G.; Wemmie, John A)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper.  I had the primary contributions to the interpretation of validation results for this work.  I contributed torevising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}},
author = {Shao, Wei and Christensen, Gary E. and Johnson, Hans J. and Song, Joo H. and Durumeric, Oguz C. and Johnson, Casey P. and Shaffer, Joseph J. and Magnotta, Vincent A. and Fiedorowicz, Jess G. and Wemmie, John A.},
booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
doi = {10.1109/CVPRW.2016.75},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Shao et al/Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops/Shao et al. - 2016 - Population Shape Collapse in Large Deformation Registration of MR Brain Images.pdf:pdf},
isbn = {9781467388504},
issn = {21607516},
keywords = {Diffeomorphic image registration,Shape collapse},
pages = {549--557},
title = {{Population Shape Collapse in Large Deformation Registration of MR Brain Images}},
year = {2016}
}
@article{hawley2010introduction,
author = {Lu, Wei and Johnson, Hans and Hawley, J and Johnson, Hans and Dowling, J and Malaterre, M and Greer, P B and Salvado, O},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Lu et al/Insight Journal/Lu et al. - 2010 - Introduction to ITK resample in-place image filter.pdf:pdf},
journal = {Insight Journal},
pages = {3--6},
title = {{Introduction to ITK resample in-place image filter}},
url = {http://ir.uiowa.edu/etd/851/},
year = {2010}
}
@inproceedings{ghayoor2016tissue,
abstract = {This paper describes enhancements to automate classification of brain tissues formulti-site degenerative magnetic resonance imaging (MRI) data analysis. Processing of large collections of MR images is a key research technique to advance our understanding of the human brain. Previous studies have developed a robust multi-modal tool for automated tissue classification of large-scale data based on expectation maximization (EM) method initialized by group-wise prior probability distributions. This work aims to augment the EM-based classification using a non-parametric fuzzy k-Nearest Neighbor (k-NN) classifier that can model the unique anatomical states of each subject in the study of degenerative diseases. The presented method is applicable to multi-center heterogeneous data analysis and is quantitatively validated on a set of 18 synthetic multi-modal MR datasets having six different levels of noise and three degrees of bias-field provided with known ground truth. Dice index and average Hausdorff distance are used to compare the accuracy and robustness of the proposed method to a state-of-the-art classification method implemented based on EM algorithm. Both evaluation measurements show that presented enhancements produce superior results as compared to the EM only classification.},
annote = {From Duplicate 1 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S.; Kim, Regina EY Y; Johnson, Hans J.)

From Duplicate 1 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S.; Kim, Regina EY; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S; Kim, Regina E Y; Johnson, Hans J)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper. I had contributions to the software methods development, interpretation of validation results for this work. I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process. :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S.; Kim, Regina EY Y; Johnson, Hans J.)

From Duplicate 1 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S; Kim, Regina E Y; Johnson, Hans J)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper. I had contributions to the software methods development, interpretation of validation results for this work. I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process. :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S.; Kim, Regina EY Y; Johnson, Hans J.)

From Duplicate 1 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S.; Kim, Regina EY Y; Johnson, Hans J.)

From Duplicate 1 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S.; Kim, Regina EY; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S; Kim, Regina E Y; Johnson, Hans J)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper. I had contributions to the software methods development, interpretation of validation results for this work. I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process. :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}

From Duplicate 2 (Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method - Ghayoor, Ali; Paulsen, Jane S.; Kim, Regina EY; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}},
author = {Ghayoor, Ali and Paulsen, Jane S. and Kim, Regina E. Y. and Johnson, Hans J.},
booktitle = {Medical Imaging 2016: Image Processing},
doi = {10.1117/12.2216625},
editor = {Styner, Martin A. and Angelini, Elsa D.},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Ghayoor et al/Medical Imaging 2016 Image Processing/Ghayoor et al. - 2016 - Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method.pdf:pdf},
isbn = {9781510600195},
issn = {16057422},
keywords = {expectation maximization,fuzzy k-nearest neighborhood method,multi-site studies,neurodegenerative diseases,segmentation,tissue classification},
month = {mar},
organization = {International Society for Optics and Photonics},
pages = {97841V},
title = {{Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method}},
url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2216625},
volume = {9784},
year = {2016}
}
@inproceedings{kim2015efficient,
abstract = {Advances in medical image applications have led to mounting expectations in regard to their impact on neuroscience studies. In light of this fact, a comprehensive application is needed to move neuroimaging data into clinical research discoveries in a way that maximizes collected data utilization and minimizes the development costs.We introduce BRAINS AutoWorkup, a Nipype based open source MRI analysis application distributed with BRAINSTools suite (http://brainsia.github. io/BRAINSTools/). This work describes the use of efficient and extensible automated brain MRI analysis workflow for large-scale multi-center longitudinal studies. We first explain benefits of our extensible workflow development using Nipype, including fast integration and validation of recently introduced tools with heterogeneous software infrastructures. Based on this workflow development, we also discuss our recent advancements to the workflow for reliable and accurate analysis of multi-center longitudinal data. In addition to Nipype providing a unified workflow, its support for High Performance Computing (HPC) resources leads to a further increased time efficiency of our workflow. We show our success on a few selected large-scale studies, and discuss future direction of this translation research in medical imaging applications.},
annote = {From Duplicate 2 (Efficient and extensible workflow: Reliable whole brain segmentation for large-scale, multi-center longitudinal human MRI analysis using high performance/throughput computing resources - Kim, Regina EY Y; Nopoulos, Peggy C.; Paulsen, Jane S.; Johnson, Hans J.)

From Duplicate 1 (Efficient and Extensible Workflow: Reliable Whole Brain Segmentation for Large-Scale, Multi-center Longitudinal Human MRI Analysis Using High Performance/Throughput Computing Resources - Kim, Regina EY Y; Nopoulos, Peggy C.; Paulsen, Jane S.; Johnson, Hans J.)

From Duplicate 1 (Efficient and extensible workflow: Reliable whole brain segmentation for large-scale, multi-center longitudinal human MRI analysis using high performance/throughput computing resources - Kim, Regina EY Y; Nopoulos, Peggy C.; Paulsen, Jane S.; Johnson, Hans J.)

From Duplicate 1 (Efficient and extensible workflow: Reliable whole brain segmentation for large-scale, multi-center longitudinal human MRI analysis using high performance/throughput computing resources - Kim, Regina EY; Nopoulos, Peggy C.; Paulsen, Jane S.; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}},
author = {Kim, Regina E.Y. and Nopoulos, Peg and Paulsen, Jane and Johnson, Hans},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-319-31808-0_7},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Kim et al/Workshop on Clinical Image-Based Procedures/Kim et al. - 2015 - Efficient and extensible workflow Reliable whole brain segmentation for large-scale, multi-center longitudinal human.pdf:pdf},
isbn = {9783319318073},
issn = {16113349},
keywords = {Brain,HPC/HTC,Large-scale,Longitudinal data,MRI,Pipeline},
organization = {Springer, Cham},
pages = {54--61},
title = {{Efficient and extensible workflow: Reliable whole brain segmentation for large-scale, multi-center longitudinal human MRI analysis using high performance/throughput computing resources}},
url = {http://link.springer.com/10.1007/978-3-319-31808-0{\_}7},
volume = {9401},
year = {2016}
}
@article{Lowekamp2013,
abstract = {SimpleITK is a new interface to the Insight Segmentation and Registration Toolkit (ITK) designed to facilitate rapid prototyping, education and scientific activities via high level programming languages. ITK is a templated C++ library of image processing algorithms and frameworks for biomedical and other applications, and it was designed to be generic, flexible and extensible. Initially, ITK provided a direct wrapping interface to languages such as Python and Tcl through the WrapITK system. Unlike WrapITK, which exposed ITK's complex templated interface, SimpleITK was designed to provide an easy to use and simplified interface to ITK's algorithms. It includes procedural methods, hides ITK's demand driven pipeline, and provides a template-less layer. Also SimpleITK provides practical conveniences such as binary distribution packages and overloaded operators. Our user-friendly design goals dictated a departure from the direct interface wrapping approach of WrapITK, toward a new facade class structure that only exposes the required functionality, hiding ITK's extensive template use. Internally SimpleITK utilizes a manual description of each filter with code-generation and advanced C++ meta-programming to provide the higher-level interface, bringing the capabilities of ITK to a wider audience. SimpleITK is licensed as open source software library under the Apache License Version 2.0 and more information about downloading it can be found at http://www.simpleitk.org. {\textcopyright} 2013 Lowek amp, Chen, Ib{\'{a}}{\~{n}}ez and Blezek.},
author = {Lowekamp, Bradley C. and Chen, David T. and Ib{\'{a}}{\~{n}}ez, Luis and Blezek, Daniel},
doi = {10.3389/fninf.2013.00045},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Lowekamp et al/Frontiers in Neuroinformatics/Lowekamp et al. - 2013 - The design of simpleITK.pdf:pdf},
isbn = {2762010004},
issn = {16625196},
journal = {Frontiers in Neuroinformatics},
keywords = {Image processing and analysis,Image processing software,Insight toolkit,Segmentation,Software design,Software development},
number = {DEC},
pages = {45},
pmid = {24416015},
title = {{The design of simpleITK}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3874546{\&}tool=pmcentrez{\&}rendertype=abstract https://www.frontiersin.org/article/10.3389/fninf.2013.00045},
volume = {7},
year = {2013}
}
@article{Kim2014,
abstract = {Machine learning (ML)-based segmentation methods are a common technique in the medical image processing field. In spite of numerous research groups that have investigated ML-based segmentation frameworks, there remains unanswered aspects of performance variability for the choice of two key components: ML algorithm and intensity normalization. This investigation reveals that the choice of those elements plays a major part in determining segmentation accuracy and generalizability. The approach we have used in this study aims to evaluate relative benefits of the two elements within a subcortical MRI segmentation framework. Experiments were conducted to contrast eight machine-learning algorithm configurations and 11 normalization strategies for our brain MR segmentation framework. For the intensity normalization, a Stable Atlas-based Mapped Prior (STAMP) was utilized to take better account of contrast along boundaries of structures. Comparing eight machine learning algorithms on down-sampled segmentation MR data, it was obvious that a significant improvement was obtained using ensemble-based ML algorithms (i.e., random forest) or ANN algorithms. Further investigation between these two algorithms also revealed that the random forest results provided exceptionally good agreement with manual delineations by experts. Additional experiments showed that the effect of STAMP-based intensity normalization also improved the robustness of segmentation for multicenter data sets. The constructed framework obtained good multicenter reliability and was successfully applied on a large multicenter MR data set (n{\textgreater}. 3000). Less than 10{\%} of automated segmentations were recommended for minimal expert intervention. These results demonstrate the feasibility of using the ML-based segmentation tools for processing large amount of multicenter MR images. We demonstrated dramatically different result profiles in segmentation accuracy according to the choice of ML algorithm and intensity normalization chosen. {\textcopyright} 2014 Elsevier Inc.},
annote = {From Duplicate 1 (Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data - Kim, Regina EY; Magnotta, Vincent A.; Liu, Dawei; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data - Kim, Regina EY; Magnotta, Vincent A.; Liu, Dawei; Johnson, Hans J.; Kim, Eun Young; Magnotta, Vincent A.; Liu, Dawei; Johnson, Hans J.)

From Duplicate 1 (Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data - Kim, Regina EY; Magnotta, Vincent A.; Liu, Dawei; Johnson, Hans J.; Kim, Eun Young; Magnotta, Vincent A.; Liu, Dawei; Johnson, Hans J.)

From Duplicate 2 (Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data - Kim, Regina EY; Magnotta, Vincent A.; Liu, Dawei; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data - Kim, Regina EY; Magnotta, Vincent A.; Liu, Dawei; Johnson, Hans J.)

{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper.  I had contributions to the software methods development, interpretation of validation results for this work.  I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}},
author = {Kim, Eun Young and Magnotta, Vincent A. and Liu, Dawei and Johnson, Hans J.},
doi = {10.1016/j.mri.2014.04.016},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Kim et al/Magnetic Resonance Imaging/Kim et al. - 2014 - Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data.pdf:pdf},
isbn = {3193213152},
issn = {18735894},
journal = {Magnetic Resonance Imaging},
keywords = {Machine learning,Multicenter study,Random forest,Segmentation},
month = {may},
number = {7},
pages = {832--844},
pmid = {24818817},
publisher = {Elsevier Inc.},
title = {{Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24818817},
volume = {32},
year = {2014}
}
@article{hong2018genetic,
abstract = {Huntington's disease (HD) is an inherited neurodegenerative disorder that causes progressive breakdown of striatal neurons. Standard white matter integrity measures like fractional anisotropy and mean diffusivity derived from diffusion tensor imaging were analyzed in prodromal-HD subjects; however, they studied either a whole brain or specific subcortical white matter structures with connections to cortical motor areas. In this work, we propose a novel analysis of a longitudinal cohort of 243 prodromal-HD individuals and 88 healthy controls who underwent two or more diffusion MRI scans as part of the PREDICT-HD study. We separately trace specific white matter fiber tracts connecting the striatum (caudate and putamen) with four cortical regions corresponding to the hand, face, trunk, and leg motor areas. A multi-tensor tractography algorithm with an isotropic volume fraction compartment allows estimating diffusion of fast-moving extra-cellular water in regions containing crossing fibers and provides quantification of a microstructural property related to tissue atrophy. The tissue atrophy rate is separately analyzed in eight cortico-striatal pathways as a function of CAG-repeats (genetic load) by statistically regressing out age effect from our cohort. The results demonstrate a statistically significant increase in isotropic volume fraction (atrophy) bilaterally in hand fiber connections to the putamen with increasing CAG-repeats, which connects the genetic abnormality (CAG-repeats) to an imaging-based microstructural marker of tissue integrity in specific white matter pathways in HD. Isotropic volume fraction measures in eight cortico-striatal pathways are also correlated significantly with total motor scores and diagnostic confidence levels, providing evidence of their relevance to HD clinical presentation.},
author = {Hong, Yi and O'Donnell, Lauren J. and Savadjiev, Peter and Zhang, Fan and Wassermann, Demian and Pasternak, Ofer and Johnson, Hans and Paulsen, Jane and Vonsattel, Jean Paul and Makris, Nikos and Westin, Carl F. and Rathi, Yogesh},
doi = {10.1002/hbm.24217},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Hong et al/Human Brain Mapping/Hong et al. - 2018 - Genetic load determines atrophy in hand cortico-striatal pathways in presymptomatic Huntington's disease.pdf:pdf},
issn = {10970193},
journal = {Human Brain Mapping},
keywords = {CAG-repeats,cortico-striatal pathways,diffusion MRI,isotropic volume fraction,prodromal-HD},
number = {10},
pages = {3871--3883},
title = {{Genetic load determines atrophy in hand cortico-striatal pathways in presymptomatic Huntington's disease}},
volume = {39},
year = {2018}
}
@book{Ibanez2003,
abstract = {Digital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images to reduce storage and transmission overheads. The text data are encrypted before interleaving with images to ensure greater security. The graphical signals are compressed and subsequently interleaved with the image. Differential pulse-code-modulation and adaptive-delta-modulation techniques are employed for data compression, and encryption and results are tabulated for a specific example.},
author = {Ibanez, Luis and Schroeder, Will and Ng, Lydia and Cates, Josh},
booktitle = {IEEE Transactions on Information Technology in Biomedicine},
doi = {10.1109/4233.966107},
isbn = {1930934106},
issn = {1089-7771},
number = {4},
pages = {539},
title = {{The ITK Software Guide: The Insight Segmentation and Registration Toolkit}},
url = {http://www.amazon.com/dp/1930934106},
volume = {5},
year = {2003}
}
@inproceedings{Ibanez2002,
abstract = {This paper describes the design and implementation of the generic framework for image registration contained in the National Library of Medicine NLM/NIH Segmentation and Registration Toolkit (ITK). The problem of image registration has been modeled here as a structure of pluggable components that can be easily interchanged. The rationale behind the framework is presented in this paper both from the image processing and software engineering points of view. ITK is an open source project that provides a platform for developing image processing and analysis applications. State of the art practices of software engineering have been used for the design, implementation and testing of the toolkit The source code can be downloaded free of charge and used in academic and commercial applications.},
author = {Ib{\'{a}}{\~{n}}ez, L. and Ng, L. and Gee, J. and Aylward, S.},
booktitle = {Proceedings - International Symposium on Biomedical Imaging},
doi = {10.1109/ISBI.2002.1029264},
isbn = {078037584X},
issn = {19458452},
pages = {345--348},
title = {{Registration patterns: The generic framework for image registration of the insight toolkit}},
volume = {2002-Janua},
year = {2002}
}
@article{Newton2011,
abstract = {Background: From teaching juniors and peers to educating patients, it is imperative for all doctors to have basic core teaching skills. The Junior Association for the Study of Medical Education (JASME) felt that a short course in the fundamentals of teaching would be well received by students. Context: This article shares the lessons from a one-day teaching course aimed at senior medical students. Qualitative feedback helped decide which aspects of the course were most valued. Intervention: The course was piloted in London. It combined interactive plenary sessions on teaching theory with practical teaching sessions. Each student taught a small group of others a basic clinical skill, and the student teacher then received extensive feedback from their peers and an experienced clinician with a special interest in medical education. There was an opportunity to re-teach part of the skill after having taken the feedback on board. Implications: Students completed questionnaires at the start and end of the day to ascertain their expectations of the course and what they found most useful. Expectations can be grouped into three main areas: students wanted to improve their teaching skills; gain teaching experience; and receive feedback on their teaching. The most valuable part of the course was being able to practise teaching and receive feedback. Keywords used to describe the feedback included 'individual', 'valuable', 'constructive', 'instant' and 'in depth'. By continuing to run similar workshops we hope that we can further encourage the teachers of tomorrow. {\textcopyright} Blackwell Publishing Ltd 2011.},
author = {Newton, Ashley and Wright, Lucie},
doi = {10.1111/j.1743-498X.2011.00453.x},
issn = {17434971},
journal = {Clinical Teacher},
number = {4},
pages = {254--257},
title = {{Teaching toolkit for medical students}},
volume = {8},
year = {2011}
}
@incollection{Johnson2014a,
abstract = {The Insight Toolkit (ITK) is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. ITK is a cross-platform software. It uses a build environment known as CMake to manage platform-specific project generation and compilation process in a platform-independent way. ITK is implemented in C++. ITK's implementation style employs generic programming, which involves the use of templates to generate, at compile-time, code that can be applied generically to any class or data-type that supports the operations used by the template. The use of C++ templating means that the code is highly efficient and many issues are discovered at compile- time, rather than at run-time during program execution. It also means that many of ITK's algorithms can be applied to arbitrary spatial dimensions and pixel types. An automated wrapping system integrated with ITK generates an interface between C++ and a high-level programming language Python. This enables rapid prototyping and faster exploration of ideas by shortening the edit-compile-execute cycle. In addition to automated wrapping, the SimpleITK project provides a streamlined interface to ITK that is available for C++, Python, Java, CSharp, R, Tcl and Ruby. Developers from around the world can use, debug, maintain, and extend the software because ITK is an open-source project. ITK uses a model of software development known as Extreme Programming. Extreme Programming collapses the usual software development methodology into a simultaneous iterative process of design-implement-test-release. The key features of Extreme Programming are communication and testing. Communication among the members of the ITK community is what helps manage the rapid evolution of the software. Testing is what keeps the software stable. An extensive testing process supported by the system known as CDash measures the quality of ITK code on a daily basis. The ITK Testing Dashboard is updated continuously, reflecting the quality of the code at any moment. The most recent version of this document is available online at http://itk.org/ItkSoftwareGuide.pdf. This book is a guide to developing soft- ware with ITK; it is the first of two companion books. This book covers building and installation, general architecture and design, as well as the process of contributing in the ITK community. The second book covers detailed design and functionality for reading and and writing images, filtering, registration, segmentation, and performing statistical analysis.},
author = {Johnson, Hans J. and Ib, Luis and Mccormick, Matthew and Consortium, Software},
booktitle = {The ITK Software GUIDE},
doi = {1�930934-15�7},
isbn = {978-1-930934-28-3},
keywords = {Guide,Registration,Segmentation},
pages = {805},
title = {{The ITK Software Guide Book 2 : Design and Functionality Fourth Edition Updated for ITK version 4.6}},
year = {2014}
}
@book{Ibanez2003a,
abstract = {Digital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images to reduce storage and transmission overheads. The text data are encrypted before interleaving with images to ensure greater security. The graphical signals are compressed and subsequently interleaved with the image. Differential pulse-code-modulation and adaptive-delta-modulation techniques are employed for data compression, and encryption and results are tabulated for a specific example.},
author = {Ibanez, Luis and Schroeder, Will and Ng, Lydia and Cates, Josh},
booktitle = {IEEE Transactions on Information Technology in Biomedicine},
doi = {10.1109/4233.966107},
isbn = {1930934106},
issn = {1089-7771},
number = {4},
pages = {539},
title = {{The ITK Software Guide: The Insight Segmentation and Registration Toolkit}},
url = {http://www.amazon.com/dp/1930934106},
volume = {5},
year = {2003}
}
@article{Papademetris2006,
abstract = {This book is an edited collection of class handouts that was written for the graduate seminar “Programming for Medical Image Analysis” (ENAS 920a). This class was taught at Yale University, Department of Biomedical Engineering, in the Fall of 2006 and again in the Spring 2009 semester. Some the comments in this draft version of the book reflect this fact. For example, see comments beginning “at Yale”. Furthermore, many of the references that will appear in the final version are still omitted. It is made available at this stage in the hope that it will be useful.},
author = {Papademetris, Xenophon and Joshi, Alark},
journal = {Yale University},
pages = {283},
title = {{An Introduction to Programming for medical image Analysis with the visualization Toolkit}},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.122.7477{\&}rep=rep1{\&}type=pdf},
year = {2006}
}
@book{Yarbs2015,
abstract = {BACKGROUND: The analysis of eye movements (EM) by eye-tracking has been carried out for several decades to investigate mood regulation, emotional information processing, and psychomotor disturbances in depressive disorders.$\backslash$nMETHOD: A systematic review of all English language PubMed articles using the terms "saccadic eye movements" OR "eye-tracking" AND "depression" OR "bipolar disorders" was conducted using PRISMA guidelines. The aim of this review was to characterize the specific alterations of EM in unipolar and bipolar depression.$\backslash$nRESULTS: Findings regarding psychomotor disturbance showed an increase in reaction time in prosaccade and antisaccade tasks in both unipolar and bipolar disorders. In both disorders, patients have been reported to have an attraction for negative emotions, especially for negative pictures in unipolar and threatening images in bipolar disorder. However, the pattern could change with aging, elderly unipolar patients disengaging key features of sad and neutral stimuli. METHODological limitations generally include small sample sizes with mixed unipolar and bipolar depressed patients.$\backslash$nCONCLUSION: Eye movement analysis can be used to discriminate patients with depressive disorders from controls, as well as patients with bipolar disorder from patients with unipolar depression. General knowledge concerning psychomotor alterations and affective regulation strategies associated with each disorder can also be gained thanks to the analysis. Future directions for research on eye movement and depression are proposed in this review.},
author = {Yarbs, Aared L and Haigh, Basil and Ettinger, Ulrich and Kumari, Veena and Crawford, Trevor J. and Flak, Vanja and Sharma, Tonmoy and Davis, Robert E. and Corr, Philip J. and Brazil, Inti A. and de Bruijn, Ellen R A and Bulten, Berend H. and von Borries, A. Katinka L and van Lankveld, Jacques J D M and Buitelaar, Jan K. and Verkes, Robbert J. and Eisenbarth, Hedwig and Alpers, Georg W. and Singler, Eric and Gillath, Omri and Bahns, Angela J. and Burghart, Hayley A. and Clifton, Charles and Ferreira, Fernanda and Henderson, John M. and Inhoff, Albrecht W. and Liversedge, Simon P. and Reichle, Erik D. and Schotter, Elizabeth R. and Carvalho, Nicolas and Laurent, Eric and Noiret, Nicolas and Chopard, Gilles and Haffen, Emmanuel and Bennabi, Djamila and Vandel, Pierre and Gregory, R L},
booktitle = {Problems of Information Transmission},
doi = {10.1016/j.biopsycho.2004.03.014},
isbn = {0749-596X},
issn = {1931-1516},
keywords = {Antisaccade,Attraction,Automatic processing,Bipolar depression,Chest,E-Z Reader,Emotion,Endophenotype,Eye movement,Eye movement control,Eye-tracking,Friendship,Keith Rayner,Mating,Negative emotionality,Neuroticism,Oculomotor control,Prosaccade (reflexive saccade,Saccade,Schizophrenia,Schizotypal personality traits,Sentences and discourses,Unipolar depression,Visual scenes,Waist-to-hip ratio,Word recognition,behavioral adaptation,dimberg,e,emotion,error positivity,error signaling,error-related negativity,eye-tracking,facial expressions,facial expressions contain information,fast and automatic processing,g,havior,http://www.archive.org/details/eyebrainpsycholo00r,information is optimized,psychopathy,relevant for social be-,scan path,the processing of facial,thunberg,thus,visually-guided saccade)},
number = {1},
pages = {860--865},
pmid = {15312695},
title = {{Third edition : revised and updated World}},
url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/a0022758{\%}0Ahttp://dx.doi.org/10.1016/j.biopsych.2008.08.011},
volume = {86},
year = {2015}
}
@article{Wang2005,
abstract = {We have successfully created a software environment in which ultrasound data can be manipulated by, ITK (the Insight Tool-Kit), in real-time. We were able to access each frame generated within the resident computer of a TerasonTM Ultrasound Machine, convert it into the ITK image format, and demonstrate the concurrent operation of ITK on the same computer by writing the images to an external hard drive. At a rate of 10 frames per second, 512 by 512 pixel grayscale frames were written by ITK methods to the external hard drive through USB 2.0 while the ultrasound scan was occurring without thrashing or delay in system performance. This simple exercise demonstrates the potential of ITK in processing ultrasound images in real-time in addition to the more traditional off-line processing.},
author = {Wang, David and Chang, Wilson and Stetten, George},
journal = {Proc MICCAI},
pages = {1--5},
title = {{Real-time ultrasound image analysis for the insight toolkit}},
url = {http://insight-journal.org/dspace/handle/1926/43},
year = {2005}
}
@article{Wang2005a,
abstract = {We have successfully created a software environment in which ultrasound data can be manipulated by, ITK (the Insight Tool-Kit), in real-time. We were able to access each frame generated within the resident computer of a TerasonTM Ultrasound Machine, convert it into the ITK image format, and demonstrate the concurrent operation of ITK on the same computer by writing the images to an external hard drive. At a rate of 10 frames per second, 512 by 512 pixel grayscale frames were written by ITK methods to the external hard drive through USB 2.0 while the ultrasound scan was occurring without thrashing or delay in system performance. This simple exercise demonstrates the potential of ITK in processing ultrasound images in real-time in addition to the more traditional off-line processing.},
author = {Wang, David and Chang, Wilson and Stetten, George},
journal = {Proc MICCAI},
pages = {1--5},
title = {{Real-time ultrasound image analysis for the insight toolkit}},
url = {http://insight-journal.org/dspace/handle/1926/43},
year = {2005}
}
@book{Johnson2015,
abstract = {The Insight Toolkit (ITK) is an open-source software toolkit for performing registra tion and segmen- tation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is a n image acquired from such medical instru- mentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT s can may be aligned with a MRI scan in order to combine the information contained in both. ITK is a cross-platform software. It uses a build environmen t known as CMake to manage platform- specific project generation and compilation process in a pla tform-independent way. ITK is imple- mented in C++. ITK's implementation style employs generic p rogramming, which involves the use of templates to generate, at compile-time, code that can be applied generically to any class or data-type that supports the operations used by the template . The use of C++ templating means that the code is highly efficient and many issues are discovered at compile-time, rather than at run-time during program execution. It also means that many of ITK's al gorithms can be applied to arbitrary spatial dimensions and pixel types. An automated wrapping system integrated with ITK generates an interface between C++ and a high- level programming language Python . This enables rapid prototyping and faster exploration of i deas by shortening the edit-compile-execute cycle. In addition to automated wrapping, the SimpleITK project provides a streamlined interface to ITK that is avai lable for C++, Python, Java, CSharp, R, Tcl and Ruby. Developers from around the world can use, debug, maintain, a nd extend the software because ITK is an open-source project. ITK uses a model of software devel opment known as Extreme Program- ming. Extreme Programming collapses the usual software dev elopment methodology into a simulta- neous iterative process of design-implement-test-releas e. The key features of Extreme Programming are communication and testing. Communication among the mem bers of the ITK community is what helps manage the rapid evolution of the software. Testing is what keeps the software stable. An extensive testing process supported by the system known as CDash measures the quality of ITK code on a daily basis. The ITK Testing Dashboard is updated co ntinuously, reflecting the quality of the code at any moment. The most recent version of this document is available online at http://itk.org/ItkSoftwareGuide.pdf . This book is a guide to developing software with ITK; it is the first of two companion books. This book cove rs building and installation, general architecture and design, as well as the process of contribut ing in the ITK community. The second book covers detailed design and functionality for reading a nd writing images, filtering, registration, segmentation, and performing statistical analysis.},
author = {Johnson, Hans J. and McCormick, Matthew M and Ibanez, Luis},
booktitle = {Kitware, Inc.(January 2015)},
isbn = {1930934270},
keywords = {Guide,Registration,Segmentation},
pages = {248},
title = {{The ITK Software Guide: Introduction and Development Guidelines version 4.6}},
url = {https://itk.org/},
year = {2015}
}
@article{Ng2003,
abstract = {This paper introduces the new concept of narrow-band to image registration. Narrow-banding is a common technique used in the solution of level set approaches to image processing. For our application, the narrow-band describes the shape of an object by using a data structure containing the signed distance values at a small band of neighboring pixels. This compact representation of an object is well suited for performing registration against a standard image as well as against another narrow-band. The novel technique was implemented in the registration framework of the NLM Insight Toolkit (ITK). This implementation illustrates the great advantage of a modular framework structure that allows researchers to concentrate in the interesting aspects of a new algorithm by building on an existing set of predefined components for providing the rest of standard functionalities that are required. {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.},
author = {Ng, Lydia and Ib{\'{a}}{\~{n}}ez, Luis},
doi = {10.1007/978-3-540-39701-4_29},
issn = {03029743},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {271--280},
title = {{Narrow band to image registration in the insight toolkit}},
volume = {2717},
year = {2003}
}
@inproceedings{Muller2006,
abstract = {Visual information retrieval is an emerging domain in the medical field as it has been in computer vision for more than ten years. It has the potential to help better managing the rising amount of visual medical data. One of the most frequent application fields for content-based medical image retrieval (CBIR) is diagnostic aid. By submitting an image showing a certain pathology to a CBIR system, the medical expert can easily find similar cases. A major problem is the background surrounding the object in many medical images. System parameters of the imaging modalities are stored around the images in text as well as patient name or a logo of the institution. With such noisy input data, image retrieval often rather finds images where the object appears in the same area and is surrounded by similar structures. Whereas in specialised application domains, segmentation can focus the research on a particular area, PACS-like (Picture Archiving and Communication System) databases containing a large variety of images need a more general approach. This article describes an algorithm to extract the important object of the image to reduce the amount of data to be analysed for CBIR and focuses analysis to the important object. Most current solutions index the entire image without making a difference between object and background when using varied PACS-like databases or radiology teaching files. Our requirement is to have a fully automatic algorithm for object extraction. Medical images have the advantage to normally have one particular object more or less in the centre of the image. The database used for evaluating this task is taken from a radiology teaching file called casimage and the retrieval component is an open source retrieval engine called medGIFT. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
author = {M{\"{u}}ller, Henning and Heuberger, Joris and Depeursinge, Adrien and Geissb{\"{u}}hler, Antoine},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/11880592_36},
isbn = {3540457801},
issn = {16113349},
pages = {476--488},
title = {{Automated object extraction for medical image retrieval using the Insight Toolkit (ITK)}},
volume = {4182 LNCS},
year = {2006}
}
@book{Higham2016,
abstract = {Third edition. "MATLAB is an interactive system for numerical computation that is widely used for teaching and research in industry and academia. It provides a modern programming language and problem solving environment, with powerful data structures, customizable graphics, and easy-to-use editing and debugging tools. This third edition of MATLAB Guide completely revises and updates the best-selling second edition and is more than 25 percent longer. The book remains a lively, concise introduction to the most popular and important features of MATLAB and the Symbolic Math Toolbox. Key features are a tutorial in Chapter 1 that gives a hands-on overview of MATLAB, a thorough treatment of MATLAB mathematics, including the linear algebra and numerical analysis functions and the differential equation solvers, and a web page that provides a link to example program files, updates, and links to MATLAB resources. The new edition contains color figures throughout, includes pithy discussions of related topics in new "Asides" boxes that augment the text, has new chapters on the Parallel Computing Toolbox, object-oriented programming, graphs, and large data sets, covers important new MATLAB data types such as categorical arrays, string arrays, tall arrays, tables, and timetables, contains more on MATLAB workflow, including the Live Editor and unit tests, and fully reflects major updates to the MATLAB graphics system." Chapter 1: A Brief Tutorial -- Chapter 2: Basics -- Chapter 3: Distinctive Features of MATLAB -- Chapter 4: Arithmetic -- Chapter 5: Matrices -- Chapter 6: Operators and Flow Control -- Chapter 7: Program Files -- Chapter 8: Graphics -- Chapter 9: Linear Algebra -- Chapter 10: More on Functions -- Chapter 11: Numerical Methods: Part I -- Chapter 12: Numerical Methods: Part II -- Chapter 13: Input and Output -- Chapter 14: Troubleshooting -- Chapter 15: Sparse Matrices -- Chapter 16: More on Coding -- Chapter 17: Advanced Graphics -- Chapter 18: Other Data Types and Multidimensional Arrays -- Chapter 19: Object-Oriented Programming -- Chapter 20: The Symbolic Math Toolbox -- Chapter 21: Graphs -- Chapter 22: Large Data Sets -- Chapter 23: Optimizing Codes -- Chapter 24: Tricks and Tips -- Chapter 25: The Parallel Computing Toolbox -- Chapter 26: Case Studies.},
author = {Higham, Desmond J. and Higham, Nicholas J.},
booktitle = {MATLAB Guide, Third Edition},
doi = {10.1137/1.9781611974669},
title = {{MATLAB Guide, Third Edition}},
year = {2016}
}
@incollection{Hanssen2002,
author = {Hanssen, Nils and von Rymon-Lipinski, Bartosz and Jansen, Thomas and Li{\'{e}}vin, Marc and Keeve, Erwin},
booktitle = {CARS 2002 Computer Assisted Radiology and Surgery},
doi = {10.1007/978-3-642-56168-9_74},
pages = {445--449},
title = {{Integrating the Insight Toolkit itk into a medical software framework}},
year = {2002}
}
@article{Avants2014a,
abstract = {Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit (ITK4) seeks to establish new standards in publicly available image registration methodology. ITK4 makes several advances in comparison to previous versions of ITK. ITK4 supports both multivariate images and objective functions; it also unifies high-dimensional (deformation field) and low-dimensional (affine) transformations with metrics that are reusable across transform types and with composite transforms that allow arbitrary series of geometric mappings to be chained together seamlessly. Metrics and optimizers take advantage of multi-core resources, when available. Furthermore, ITK4 reduces the parameter optimization burden via principled heuristics that automatically set scaling across disparate parameter types (rotations vs. translations). A related approach also constrains steps sizes for gradient-based optimizers. The result is that tuning for different metrics and/or image pairs is rarely necessary allowing the researcher to more easily focus on design/comparison of registration strategies. In total, the ITK4 contribution is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. Finally, we contextualize this work with a reference registration evaluation study with application to pediatric brain labeling. {\textcopyright} 2014 Avants, Tustison, Stauffer, Song, Wuand Gee.},
author = {Avants, Brian B. and Tustison, Nicholas J. and Stauffer, Michael and Song, Gang and Wu, Baohua and Gee, James C.},
doi = {10.3389/fninf.2014.00044},
issn = {16625196},
journal = {Frontiers in Neuroinformatics},
keywords = {Brain,Death,MRI,Open-source,Registration},
number = {APR},
title = {{The Insight ToolKit image registration framework}},
volume = {8},
year = {2014}
}
@misc{Ibanez2005,
abstract = {Everything you need to install, use, and extend the Insight Segmentation and Registration Toolikit ITK. Includes detailed examples, installation procedures, and system overview for ITK version 2.4. (The included examples are taken directly from the ITK source code repository and are designed to demonstrate the essential features of the software.) The book comes with a CD-ROM that contains a complete hyperlinked version of the book plus ITK source code, data, Windows binaries, and extensive class documentation. Also includes CMake binaries for managing the ITK build process on a variety of compiler and operating system configurations.},
author = {Ibanez, L and Schroeder, W and Ng, L and Cates, J},
booktitle = {The ITK Software Guide},
doi = {1�930934-15�7},
isbn = {1930934157},
issn = {10445323},
number = {May},
pages = {804},
pmid = {1000070720},
title = {{The ITK Software Guide}},
url = {http://www.itk.org/ItkSoftwareGuide.pdf},
volume = {Second},
year = {2005}
}
@article{Shelton2005,
abstract = {We present several case studies which examine the role that the Insight Toolkit (ITK) played in three medical image analysis courses and several conference tutorials. These courses represent the first use of ITK in a teaching environment, and we believe that a discussion of the teaching approach in each case and the benefits and challenges of ITK will be useful to future medical image analysis course development. ITK was found to provide significant value in a classroom setting since it provides both working "canned" algorithms, including some recently developed methods that are unavailable elsewhere, as well as a framework for developing new techniques and applications. Several areas of difficulty, particularly in regards to code complexity and advanced object-oriented design techniques, have been identified which may make the learning curve of ITK somewhat more complex than a language such as Matlab™. {\textcopyright} 2005 Elsevier B.V. All rights reserved.},
author = {Shelton, Damion and Stetten, George and Aylward, Stephen and Ib{\'{a}}{\~{n}}ez, Luis and Cois, Aaron and Stewart, Charles},
doi = {10.1016/j.media.2005.04.011},
issn = {13618415},
journal = {Medical Image Analysis},
keywords = {Image analysis,Insight Toolkit,Teaching},
number = {6},
pages = {605--611},
title = {{Teaching medical image analysis with the Insight Toolkit}},
volume = {9},
year = {2005}
}
@book{Johnson2015a,
abstract = {The Insight Toolkit (ITK) is an open-source software toolkit for performing registration and segmen- tation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired fromsuchmedical instru- mentation as CT orMRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in themedical environment, a CT scan may be aligned with aMRI scan in order to combine the information contained in both.},
author = {Johnson, Hans J. and McCormick, Matthew M and Ibanez, Luis},
booktitle = {Kitware, Inc.(January 2015)},
isbn = {978-1930934276},
title = {{The ITK Software Guide Book 1: Introduction and Development Guidelines Fourth Edition Updated for ITK version 4.7}},
url = {https://itk.org/},
year = {2015}
}
@inproceedings{Tsai2006,
abstract = {This paper presents a correspondence-based toolkit for image registration. Written in C++, the toolkit complements the capabilities of the Insight Toolkit (ITK). Major components include features, feature sets, match generators, error scale estimators, robust transformation estimators, and convergence testers, all combined and controlled by several different registration engines. Correspondence-based algorithms which can be implemented using the toolkit extend from ICP to hybrids of intensity-based and feature-based registration. The toolkit is being used both as an education tool and the foundation for developing new algorithms. {\textcopyright} 2006 IEEE.},
author = {Tsai, Chia Ling and Stewart, Charles V. and Perera, Amitha and Lee, Ying Lin and Yang, Gehua and Sofka, Michal},
booktitle = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
doi = {10.1109/ICSMC.2006.384753},
isbn = {1424401003},
issn = {1062922X},
pages = {3972--3977},
title = {{A correspondence-based software toolkit for image registration}},
volume = {5},
year = {2006}
}
@book{Ibanez2005a,
abstract = {The Insight Toolkit (ITK) is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digi- tally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task ofaligning or de- veloping correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. ITK is implemented in C++. It is cross-platform, using a build environment known as CMake to manage the compilation process in a platform-independent way. In addition, an automated wrapping process (Cable) generates interfaces between C++ and interpreted programming lan- guages such as Tcl, Java, and Python. This enables developers to create software using a variety of programming languages. ITK's C++ implementation style is referred to as generic program- ming, which is to say that it uses templates so that the same code can be applied generically to any class or type that happens to support the operations used. Such C++ templating means that the code is highly efficient, and that many software problems are discovered at compile-time, rather than at run-time during program execution. Because ITK is an open-source project, developers from around the world can use, debug, main- tain, and extend the software. ITK uses a model ofsoftware development referred to as Extreme Programming. Extreme Programming collapses the usual software creation methodology into a simultaneous and iterative process of design-implement-test-release. The key features ofEx- treme Programming are communication and testing. Communication among the members of the ITK community is what helps manage the rapid evolution of the software. Testing is what keeps the software stable. In ITK, an extensive testing process (using a system known as Dart) is in place that measures the quality on a daily basis. The ITK Testing Dashboard is posted continuously, reflecting the quality of the software at any moment. This book is a guide to using and developing with ITK. The sample code in the directory pro- vides a companion to the material presented here. The most recent version of this document is available online at http://www.itk.org/ItkSoftwareGuide.pdf.},
author = {Ibanez, Luis and Schroeder, Will and Ng, Lydia and Cates, Josh},
booktitle = {Insight Software Consortium},
doi = {10.1016/S0014-5793(02)03066-1},
issn = {00145793},
keywords = {ITK,Software},
title = {{The ITK Software Guide, 2nd Edition}},
year = {2005}
}
@article{Pierson2011,
abstract = {The BRAINS (Brain Research: Analysis of Images, Networks, and Systems) image analysis software has been in use, and in constant development, for over 20. years. The original neuroimage analysis pipeline using BRAINS was designed as a semiautomated procedure to measure volumes of the cerebral lobes and subcortical structures, requiring manual intervention at several stages in the process. Through use of advanced image processing algorithms the need for manual intervention at stages of image realignment, tissue sampling, and mask editing have been eliminated. In addition, inhomogeneity correction, intensity normalization, and mask cleaning routines have been added to improve the accuracy and consistency of the results. The fully automated method, AutoWorkup, is shown in this study to be more reliable (ICC ≥ 0.96, Jaccard index ≥ 0.80, and Dice index ≥ 0.89 for all tissues in all regions) than the average of 18 manual raters. On a set of 1130 good quality scans, the failure rate for correct realignment was 1.1{\%}, and manual editing of the brain mask was required on 4{\%} of the scans. In other tests, AutoWorkup is shown to produce measures that are reliable for data acquired across scanners, scanner vendors, and across sequences. Application of AutoWorkup for the analysis of data from the 32-site, multivendor PREDICT-HD study yield estimates of reliability to be greater than or equal to 0.90 for all tissues and regions. {\textcopyright} 2010 Elsevier Inc.},
annote = {{\#}{\#}CONTRIBUTIONS: I developed custom analysis software to achieve the desired interpretation of results.   I had substantial contributions to the software methods development, interpretation of validation results for this work.  I assisted with critically reviewing and revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}},
author = {Pierson, Ronald K. and Johnson, Hans J. and Harris, Gregory and Keefe, Helen and Paulsen, Jane S. and Andreasen, Nancy C. and Magnotta, Vincent A.},
doi = {10.1016/j.neuroimage.2010.06.047},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Pierson et al/NeuroImage/Pierson et al. - 2011 - Fully automated analysis using BRAINS AutoWorkup.pdf:pdf},
isbn = {1095-9572 (Electronic)$\backslash$n1053-8119 (Linking)},
issn = {10538119},
journal = {NeuroImage},
keywords = {Automated image analysis,BRAINS,Morphometry,Pipeline,Segmentation,Volumetric analysis},
month = {jan},
number = {1},
pages = {328--336},
pmid = {20600977},
publisher = {Elsevier Inc.},
title = {{Fully automated analysis using BRAINS: AutoWorkup}},
url = {http://www.sciencedirect.com/science/article/pii/S1053811910009055 papers2://publication/uuid/35F9A8B8-DD4A-4857-903F-16D3F7CCAEF7 http://www.ncbi.nlm.nih.gov/pubmed/20600977},
volume = {54},
year = {2011}
}
@inproceedings{Gerard2016,
abstract = {Lung segmentation is a critical initial step in planning radiation therapy interventions for lung cancer patients. Achieving robust automatic segmentation of lungs with large tumors is challenging due to large variations in lung morphology, tumor location, and tumor shape between subjects. We present an automatic method to segment lungs with large tumors in CT images using an initial intensity based segmentation followed by alpha shape construction and graph search. We evaluated our method by comparing automated segmentations to manual segmentations on twelve subjects. Computed metrics for segmentation quality include average surface distance of 0.727 mm and average DICE coefficient of 0.970. These results demonstrate that the proposed method accurately segments the entire lung regions both free of and in the presence of large tumors.},
annote = {{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper. I was responsible for the data processing for one of the evaluated algorithms. I had the primary contributions to the interpretation of validation results for this work.  I contributed to revising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}},
author = {Gerard, Sarah E and Johnson, Hans J. and Bayouth, John E and Christensen, Gary E. and Du, Kaifang and Guo, Junfeng and Reinhardt, Joseph M},
booktitle = {PIA: Pulmonary Image Analysis},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Gerard et al/PIA Pulmonary Image Analysis/Gerard et al. - 2016 - Alpha Shapes for Lung Segmentation in the Presence of Large Tumors.pdf:pdf},
pages = {1--9},
title = {{Alpha Shapes for Lung Segmentation in the Presence of Large Tumors}},
year = {2016}
}
@techreport{Kim2011,
annote = {From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K; Kim EYR Johnson HJ, Williams N K)

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K)

From Duplicate 3 ( Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young; Johnson, Hans J.; Williams, Norman K )

From Duplicate 3 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Regina E Y; Johnson, Hans J.; Williams, Norman K)

From Duplicate 1 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K; Kim EYR Johnson HJ, Williams N K)

From Duplicate 2 (Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young Regina; Johnson, Hans J.; Williams, Norman K)

From Duplicate 3 ( Affine Transformation for Landmark Based Registration Initializer in ITK - Kim, Eun Young; Johnson, Hans J.; Williams, Norman K )},
author = {Kim, Regina E Y and Johnson, Hans J and Williams, Norman K and Kim, Eun Young Regina and {Kim EYR Johnson HJ}, Williams N K},
booktitle = {Engineering},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Kim et al/Engineering/Kim et al. - 2011 - Affine Transformation for Landmark Based Registration Initializer in ITK.pdf:pdf},
keywords = {Affine Transformation for Landmark Based Registrat,Insight Journal},
mendeley-tags = {Insight Journal},
pages = {1--8},
title = {{Affine Transformation for Landmark Based Registration Initializer in ITK}},
year = {2011}
}
@article{johnson2015itk,
abstract = {The Insight Toolkit (ITK) is an open-source software toolkit for performing registra tion and segmen- tation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is a n image acquired from such medical instru- mentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT s can may be aligned with a MRI scan in order to combine the information contained in both. ITK is a cross-platform software. It uses a build environmen t known as CMake to manage platform- specific project generation and compilation process in a pla tform-independent way. ITK is imple- mented in C++. ITK's implementation style employs generic p rogramming, which involves the use of templates to generate, at compile-time, code that can be applied generically to any class or data-type that supports the operations used by the template . The use of C++ templating means that the code is highly efficient and many issues are discovered at compile-time, rather than at run-time during program execution. It also means that many of ITK's al gorithms can be applied to arbitrary spatial dimensions and pixel types. An automated wrapping system integrated with ITK generates an interface between C++ and a high- level programming language Python . This enables rapid prototyping and faster exploration of i deas by shortening the edit-compile-execute cycle. In addition to automated wrapping, the SimpleITK project provides a streamlined interface to ITK that is avai lable for C++, Python, Java, CSharp, R, Tcl and Ruby. Developers from around the world can use, debug, maintain, a nd extend the software because ITK is an open-source project. ITK uses a model of software devel opment known as Extreme Program- ming. Extreme Programming collapses the usual software dev elopment methodology into a simulta- neous iterative process of design-implement-test-releas e. The key features of Extreme Programming are communication and testing. Communication among the mem bers of the ITK community is what helps manage the rapid evolution of the software. Testing is what keeps the software stable. An extensive testing process supported by the system known as CDash measures the quality of ITK code on a daily basis. The ITK Testing Dashboard is updated co ntinuously, reflecting the quality of the code at any moment. The most recent version of this document is available online at http://itk.org/ItkSoftwareGuide.pdf . This book is a guide to developing software with ITK; it is the first of two companion books. This book cove rs building and installation, general architecture and design, as well as the process of contribut ing in the ITK community. The second book covers detailed design and functionality for reading a nd writing images, filtering, registration, segmentation, and performing statistical analysis.},
author = {Johnson, Hans J. and Mccormick, Matthew M and Ibanez, Luis},
isbn = {978-1-930934-28-3},
keywords = {Guide,Registration,Segmentation},
publisher = {Kitware, Inc.},
title = {{The ITK Software Guide Book 1: Introduction and Development Guidelines-Volume 1}},
year = {2015}
}
@article{forbes2016open,
abstract = {The creation of high-quality medical imaging reference atlas datasets with consistent dense anatomical region labels is a challenging task. Reference atlases have many uses in medical image applications and are essential components of atlas-based segmentation tools commonly used for producing personalized anatomical measurements for individual subjects. The process of manual identification of anatomical regions by experts is regarded as a so-called gold standard; however, it is usually impractical because of the labor-intensive costs. Further, as the number of regions of interest increases, these manually created atlases often contain many small inconsistently labeled or disconnected regions that need to be identified and corrected. This project proposes an efficient process to drastically reduce the time necessary for manual revision in order to improve atlas label quality. We introduce the LabelAtlasEditor tool, a SimpleITK-based open-source label atlas correction tool distributed within the image visualization software 3D Slicer. LabelAtlasEditor incorporates several 3D Slicer widgets into one consistent interface and provides label-specific correction tools, allowing for rapid identification, navigation, and modification of the small, disconnected erroneous labels within an atlas. The technical details for the implementation and performance of LabelAtlasEditor are demonstrated using an application of improving a set of 20 Huntingtons Disease-specific multi-modal brain atlases. Additionally, we present the advantages and limitations of automatic atlas correction. After the correction of atlas inconsistencies and small, disconnected regions, the number of unidentified voxels for each dataset was reduced on average by 68.48{\%}.},
annote = {{\#}{\#}CONTRIBUTIONS: I was the primary mentor for all aspects of this project. I secondarily responsible for this paper. I had contributions to the software methods development, interpretation of validation results for this work. I was the primary author of the manuscript and oversaw revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process. :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}},
author = {Forbes, Jessica L and Kim, Regina E Y and Paulsen, Jane S and Johnson, Hans J},
doi = {10.3389/fninf.2016.00029},
issn = {1662-5196},
journal = {Frontiers in neuroinformatics},
keywords = {Huntingtons Disease,ITK,brain MRI,label atlas,multi-atlas,multi-modal,open-source},
month = {aug},
pages = {1--11},
pmid = {27536233},
publisher = {Frontiers Media SA},
title = {{An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases.}},
url = {http://journal.frontiersin.org/Article/10.3389/fninf.2016.00029/abstract{\%}5Cnhttp://www.ncbi.nlm.nih.gov/pubmed/27536233{\%}5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4971025},
volume = {10},
year = {2016}
}
@incollection{Avants2012,
abstract = {Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and may improve reproducibility. Version 4 of the Insight ToolKit ( ITK 4 ) seeks to establish new standards in publicly available image registration methodology. In this work, we provide an overview and preliminary evaluation of the revised toolkit against registration based on the previous major ITK version (3.20). Furthermore, we propose a nomenclature that may be used to discuss registration frameworks via schematic representations. In total, the ITK 4 contribution is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build.},
author = {Avants, Brian B and Tustison, Nicholas J and Song, Gang and Wu, Baohua and Stauffer, Michael and McCormick, Matthew M and Johnson, Hans J and Gee, James C},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
chapter = {A Unified},
doi = {10.1007/978-3-642-31340-0_28},
edition = {5th Intern},
editor = {{Dawant, B.; Christensen, G.E.; Fitzpatrick, J.M.; Rueckert}, D (Eds.},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Avants et al/Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)/Avants et al. - 2012 - A unified image registration framework for ITK.pdf:pdf},
isbn = {9783642313394},
issn = {03029743},
number = {LNCS 7359},
organization = {Springer Berlin Heidelberg},
pages = {266--275},
publisher = {Springer Berlin Heidelberg},
title = {{A unified image registration framework for ITK}},
url = {http://link.springer.com/10.1007/978-3-642-31340-0{\_}28 http://link.springer.com/chapter/10.1007{\%}2F978-3-642-31340-0{\_}28},
volume = {7359 LNCS},
year = {2012}
}
@inproceedings{shao2016population,
annote = {From Duplicate 1 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E.; Johnson, Hans J.; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P.; Shaffer, Joseph J.; Magnotta, Vincent A.; Fiedorowicz, Jess G.; Wemmie, John A)

From Duplicate 1 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E; Johnson, Hans J; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P; Shaffer, Joseph J; Magnotta, Vincent A; Fiedorowicz, Jess G; Wemmie, John A)

From Duplicate 1 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E; Johnson, Hans J; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P; Shaffer, Joseph J; Magnotta, Vincent A; Fiedorowicz, Jess G; Wemmie, John A)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper. I had the primary contributions to the interpretation of validation results for this work. I contributed torevising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E.; Johnson, Hans J.; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P.; Shaffer, Joseph J.; Magnotta, Vincent A.; Fiedorowicz, Jess G.; Wemmie, John A)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper.  I had the primary contributions to the interpretation of validation results for this work.  I contributed torevising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}

From Duplicate 2 (Population Shape Collapse in Large Deformation Registration of MR Brain Images - Shao, Wei; Christensen, Gary E.; Johnson, Hans J.; Hyun Song, Joo; Durumeric, Oguz C; Johnson, Casey P.; Shaffer, Joseph J.; Magnotta, Vincent A.; Fiedorowicz, Jess G.; Wemmie, John A)

{\#}{\#}CONTRIBUTIONS: I assisted with methods development and data analysis for this paper.  I had the primary contributions to the interpretation of validation results for this work.  I contributed torevising the intellectual content of the medical imaging methods applied.:{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Journal :{\#}{\#}},
author = {Shao, Wei and Christensen, Gary E. and Johnson, Hans J. and {Hyun Song}, Joo and Durumeric, Oguz C and Johnson, Casey P. and Shaffer, Joseph J. and Magnotta, Vincent A. and Fiedorowicz, Jess G. and Wemmie, John A},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
doi = {10.1109/CVPRW.2016.75},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Shao et al/Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops/Shao et al. - 2016 - Population Shape Collapse in Large Deformation Registration of MR Brain Images.pdf:pdf},
isbn = {9781509014378},
pages = {109--117},
title = {{Population Shape Collapse in Large Deformation Registration of MR Brain Images}},
year = {2016}
}
@article{oguz2014dtiprep,
abstract = {In the last decade, diffusion MRI (dMRI) studies of the human and animal brain have been used to investigate a multitude of pathologies and drug-related effects in neuroscience research. Study after study identifies white matter (WM) degeneration as a crucial biomarker for all these diseases. The tool of choice for studying WM is dMRI. However, dMRI has inherently low signal-to-noise ratio and its acquisition requires a relatively long scan time; in fact, the high loads required occasionally stress scanner hardware past the point of physical failure. As a result, many types of artifacts implicate the quality of diffusion imagery. Using these complex scans containing artifacts without quality control (QC) can result in considerable error and bias in the subsequent analysis, negatively affecting the results of research studies using them. However, dMRI QC remains an under-recognized issue in the dMRI community as there are no user-friendly tools commonly available to comprehensively address the issue of dMRI QC. As a result, current dMRI studies often perform a poor job at dMRI QC. Thorough QC of dMRI will reduce measurement noise and improve reproducibility, and sensitivity in neuroimaging studies; this will allow researchers to more fully exploit the power of the dMRI technique and will ultimately advance neuroscience. Therefore, in this manuscript, we present our open-source software, DTIPrep, as a unified, user friendly platform for thorough QC of dMRI data. These include artifacts caused by eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, as well as slice-wise and gradient-wise intensity inconsistencies. This paper summarizes a basic set of features of DTIPrep described earlier and focuses on newly added capabilities related to directional artifacts and bias analysis.},
annote = {From Duplicate 1 (DTIPrep: quality control of diffusion-weighted images - Oguz, Ipek; Farzinfar, Mahshid; Matsui, Joy T.; Budin, Francois; Liu, Zhexing; Gerig, Guido; Johnson, Hans J.; Styner, Martin)

From Duplicate 2 (DTIPrep: quality control of diffusion-weighted images - Oguz, Ipek; Farzinfar, Mahshid; Matsui, Joy T.; Budin, Francois; Liu, Zhexing; Gerig, Guido; Johnson, Hans J.; Styner, Martin)

{\#}{\#}CONTRIBUTIONS: As a member of the National Alliance for Medical Imaging Computing (NAMIC) I collaborated on many software engineering projets.   I had substantial contributions to the software methods development, interpretation of validation results for this work.  I assisted with critically reviewing and revising the intellectual content of the medical imaging methods applied, and their interpretation with respect to the multi-site nature of the data collection process.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Conference :{\#}{\#}},
author = {Oguz, Ipek and Farzinfar, Mahshid and Matsui, Joy T. and Budin, Francois and Liu, Zhexing and Gerig, Guido and Johnson, Hans J. and Styner, Martin},
doi = {10.3389/fninf.2014.00004},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Oguz et al/Frontiers in neuroinformatics/Oguz et al. - 2014 - DTIPrep quality control of diffusion-weighted images.pdf:pdf},
isbn = {1662-5196 (Electronic){\$}\backslash{\$}r1662-5196 (Linking)},
issn = {1662-5196},
journal = {Frontiers in neuroinformatics},
keywords = {diffusion MRI,diffusion mri,diffusion tensor imaging,open-source,preprocessing,quality c,quality control,software},
number = {January},
pages = {4},
pmid = {24523693},
publisher = {Frontiers Media SA},
title = {{DTIPrep: quality control of diffusion-weighted images}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3906573{\&}tool=pmcentrez{\&}rendertype=abstract http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00004/full http://journal.frontiersin.org/article/10.3389/fninf.2014.00004/abstract{\%}0Ahttp://www},
volume = {8},
year = {2014}
}
@article{yaniv2018simpleitk,
author = {Yaniv, Ziv and Lowekamp, Bradley C and Johnson, Hans J and Beare, Richard},
journal = {Journal of digital imaging},
number = {3},
pages = {290--303},
publisher = {Springer International Publishing},
title = {{SimpleITK image-analysis notebooks: a collaborative environment for education and reproducible research}},
volume = {31},
year = {2018}
}
@article{hawley2010introduction,
author = {Hawley, J and Johnson, H and Dowling, J and Malaterre, M and Greer, P B and Salvado, O},
journal = {Insight Journal},
title = {{Introduction to ITK resample in-place image filter}},
year = {2010}
}
@inproceedings{kim2015efficient,
author = {Kim, Regina E Y and Nopoulos, Peg and Paulsen, Jane and Johnson, Hans},
booktitle = {Workshop on Clinical Image-Based Procedures},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Kim et al/Workshop on Clinical Image-Based Procedures/Kim et al. - 2015 - Efficient and extensible workflow Reliable whole brain segmentation for large-scale, multi-center longitudinal human.pdf:pdf},
organization = {Springer, Cham},
pages = {54--61},
title = {{Efficient and extensible workflow: Reliable whole brain segmentation for large-scale, multi-center longitudinal human MRI analysis using high performance/throughput computing resources}},
year = {2015}
}
@article{avants2015neuroinformatics,
annote = {From Duplicate 1 (Neuroinformatics and the The Insight ToolKit - Avants, Brian B.; Johnson, Hans J.; Tustison, Nicholas J.)

{\#}{\#}CONTRIBUTIONS: As a contributing guest editor for a special issue of "Frontiers in Neruoinformatics" I had substantial oversight in the criteria for selection, review and ultimate publication of materials in the issue.  :{\#}{\#}
{\#}{\#}JOURNAL{\_}TYPE: Editorial :{\#}{\#}},
author = {Avants, Brian B. and Johnson, Hans J. and Tustison, Nicholas J.},
doi = {10.3389/fninf.2015.00005},
file = {:Users/johnsonhj/Documents/Mendeley Desktop/Avants, Johnson, Tustison/Frontiers in neuroinformatics/Avants, Johnson, Tustison - 2015 - Neuroinformatics and the The Insight ToolKit.pdf:pdf},
issn = {1662-5196},
journal = {Frontiers in neuroinformatics},
keywords = {C++,ITK,c,edited and reviewed by,itk,open source,registration,segmentation},
number = {March},
pages = {1--3},
pmid = {25859213},
publisher = {Frontiers Media SA},
title = {{Neuroinformatics and the The Insight ToolKit}},
url = {http://www.frontiersin.org/Neuroinformatics/10.3389/fninf.2015.00005/full},
volume = {9},
year = {2015}
}
@inbook{Stevenson2011,
address = {New York},
author = {Stevenson, G N and Collins, S L and Impey, L and Noble, J A and Ieee},
booktitle = {2011 8th Ieee International Symposium on Biomedical Imaging: From Nano to Macro},
isbn = {978-1-4244-4128-0},
pages = {891--894},
publisher = {Ieee},
series = {IEEE International Symposium on Biomedical Imaging},
title = {{SURFACE PARAMETERISATION OF THE UTERO/PLACENTAL INTERFACE USING 3D POWER DOPPLER ULTRASOUND}},
type = {Book Section},
url = {{\%}3CGo to},
year = {2011}
}
@inproceedings{Rezaei,
author = {Rezaei, A and Nuyts, J},
booktitle = {IEEE Nuclear Science Symposium Conference Record},
doi = {10.1109/NSSMIC.2013.6829031},
title = {{Joint registration of attenuation and activity images in gated TOF-PET}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904204340{\&}doi=10.1109{\%}2FNSSMIC.2013.6829031{\&}partnerID=40{\&}md5=9a3c352e39a5cde2bb765fec1156cfb1}
}
@inbook{Suter2018,
address = {New York},
author = {Suter, Y and Rummel, C and Wiest, R and Reyes, M and Ieee},
booktitle = {2018 Ieee 15th International Symposium on Biomedical Imaging},
isbn = {978-1-5386-3636-7},
pages = {1052--1055},
publisher = {Ieee},
series = {IEEE International Symposium on Biomedical Imaging},
title = {{FAST AND UNCERTAINTY-AWARE CEREBRAL CORTEX MORPHOMETRY ESTIMATION USING RANDOM FOREST REGRESSION}},
type = {Book Section},
url = {{\%}3CGo to},
year = {2018}
}
@inproceedings{Mandl,
author = {Mandl, T and Martinek, J and Mayr, W and Rattay, F and Reichel, M and Moser, E},
booktitle = {21st European Modeling and Simulation Symposium, EMSS 2009},
title = {{Towards a numerical 3D model of functional electrical stimulation of denervated, degenerated human skeletal muscle}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874185461{\&}partnerID=40{\&}md5=a9627e50e42db43cbfa2f5ca618bdad4}
}
@inbook{Zhang2014,
address = {Cham},
author = {Zhang, J and Sorby, H and Clement, J and Thomas, C D L and Hunter, P and Nielsen, P and Lloyd, D and Taylor, M and Besier, T},
booktitle = {Biomedical Simulation},
editor = {Bello, F and Cotin, S},
isbn = {978-3-319-12057-7; 978-3-319-12056-0},
pages = {182--192},
publisher = {Springer International Publishing Ag},
series = {Lecture Notes in Computer Science},
title = {{The MAP Client: User-Friendly Musculoskeletal Modelling Workflows}},
type = {Book Section},
url = {{\%}3CGo to},
volume = {8789},
year = {2014}
}
@book{Ponzio2017,
address = {Setubal},
author = {Ponzio, F and Macii, E and Ficarra, E and {Di Cataldo}, S},
doi = {10.5220/0006239201140121},
isbn = {978-989-758-215-8},
pages = {114--121},
publisher = {Scitepress},
series = {Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies, Vol 2: Bioimaging},
title = {{A Multi-modal Brain Image Registration Framework for US-guided Neuronavigation Systems Integrating MR and US for Minimally Invasive Neuroimaging}},
type = {Book},
url = {{\%}3CGo to},
year = {2017}
}
@incollection{Tourbier2014a,
author = {Tourbier, S and Bresson, X and Hagmann, P and Thiran, J P and Meuli, R and Cuadra, M B},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-319-10470-6_32},
pages = {252--259},
title = {{Efficient total variation algorithm for fetal brain MRI reconstruction}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906979454{\&}doi=10.1007{\%}2F978-3-319-10470-6{\_}32{\&}partnerID=40{\&}md5=d90bc6bfffc46271b10c0f02d0ea2f2a},
volume = {8674 LNCS},
year = {2014}
}
@inproceedings{Medina,
author = {Medina, R and Bautista, S and Morocho, V},
booktitle = {2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017},
doi = {10.1109/ETCM.2017.8247499},
pages = {1--6},
title = {{Accuracy of connected confidence left ventricle segmentation in 3-D multi-slice computerized tomography images}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045738426{\&}doi=10.1109{\%}2FETCM.2017.8247499{\&}partnerID=40{\&}md5=31a12ff7f15e6264e843b9c8cc503a7c},
volume = {2017-Janua}
}
@inproceedings{Suter,
author = {Suter, Y and Rummel, C and Wiest, R and Reyes, M},
booktitle = {Proceedings - International Symposium on Biomedical Imaging},
doi = {10.1109/ISBI.2018.8363752},
pages = {1052--1055},
title = {{Fast and uncertainty-aware cerebral cortex morphometry estimation using random forest regression}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048099086{\&}doi=10.1109{\%}2FISBI.2018.8363752{\&}partnerID=40{\&}md5=e22ed68e255aa6c6af3b32100df7276a},
volume = {2018-April}
}
@inproceedings{Kugu,
author = {Kugu, E},
booktitle = {RAST 2013 - Proceedings of 6th International Conference on Recent Advances in Space Technologies},
doi = {10.1109/RAST.2013.6581204},
pages = {217--223},
title = {{Satellite image denoising using Bilateral Filter with SPEA2 optimized parameters}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883888988{\&}doi=10.1109{\%}2FRAST.2013.6581204{\&}partnerID=40{\&}md5=eba34fda9e6d5ba7ef3f98fd189e8ed5}
}
@inproceedings{Ponzio,
author = {Ponzio, F and Macii, E and Ficarra, E and {Di Cataldo}, S},
booktitle = {

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