text:
@article{johnson2015itk,
author = {Johnson, Hans. and Ib, Luis. and Mccormick, Matthew. and {Insight Software Consortium.}},
doi = {1�930934-15�7},
isbn = {978-1-930934-28-3},
journal = {Itk},
keywords = {Guide,Registration,Segmentation},
publisher = {Kitware, Inc.},
title = {{The ITK Software Guide Book 2: Design and Functionality}},
url = {https://itk.org/ITKSoftwareGuide/html/Book2/ITKSoftwareGuide-Book2.html},
year = {2017}
}
@incollection{Johnson2014,
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{johnson2013itk,
abstract = {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. This book is a guide to developing software with ITK. It covers building and installation, ar- chitecture and design, image analysis theory and its applications, as well as the process of contributing to the ITK community. The most recent version of this document http://itk.org/ItkSoftwareGuide.pdf.},
author = {Johnson, Hans J. and Mccormick, Matt and Ibanez, Luis and Consortium, Insight Software},
title = {{The ITK Software Guide Third Edition - Updated for ITK version 4.5}},
url = {http://itk.org/ItkSoftwareGuide.pdf},
year = {2013}
}
@book{johnson2015itk,
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)},
edition = {4.7},
editor = {Johnson, Hans J. and McCormick, Matthew M and Ibanez, Luis},
isbn = {978-1930934276},
keywords = {Guide,Registration,Segmentation},
pages = {248},
publisher = {Kitware, Inc.},
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}
}
@book{johnson2014itk,
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)},
edition = {4.7},
editor = {Johnson, Hans J. and McCormick, Matthew M and Ibanez, Luis},
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}
}
@article{beare2011citk,
author = {Beare, Richard and Micevski, Daniel and Share, Chris and Parkinson, Luke and Ward, Phil and Goscinski, Wojtek and Kuiper, Mike},
journal = {The Insight Journal},
title = {{CITK-an architecture and examples of CUDA enabled ITK filters, Release 0.00}},
year = {2011}
}
@article{RN826,
abstract = {Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.},
author = {Adhikari, Bhim M. and Jahanshad, Neda and Shukla, Dinesh and Turner, Jessica and Grotegerd, Dominik and Dannlowski, Udo and Kugel, Harald and Engelen, Jennifer and Dietsche, Bruno and Krug, Axel and Kircher, Tilo and Fieremans, Els and Veraart, Jelle and Novikov, Dmitry S. and Boedhoe, Premika S.W. and van der Werf, Ysbrand D. and van den Heuvel, Odile A. and Ipser, Jonathan and Uhlmann, Anne and Stein, Dan J. and Dickie, Erin and Voineskos, Aristotle N. and Malhotra, Anil K. and Pizzagalli, Fabrizio and Calhoun, Vince D. and Waller, Lea and Veer, Ilja M. and Walter, Hernik and Buchanan, Robert W. and Glahn, David C. and Hong, L. Elliot and Thompson, Paul M. and Kochunov, Peter},
doi = {10.1007/s11682-018-9941-x},
issn = {19317565},
journal = {Brain Imaging and Behavior},
keywords = {ENIGMA EPI template,Large multi-site studies,Processing pipelines},
number = {5},
pages = {1453--1467},
title = {{A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {13},
year = {2019}
}
@article{RN973,
abstract = {The choice of a reference image typically influences the results of deformable image registration, thereby making it asymmetric. This is a consequence of a spatially non-uniform weighting in the cost function integral that leads to general registration inaccuracy. The inhomogeneous integral measure - which is the local volume change in the transformation, thus varying through the course of the registration - causes image regions to contribute differently to the objective function. More importantly, the optimization algorithm is allowed to minimize the cost function by manipulating the volume change, instead of aligning the images. The approaches that restore symmetry to deformable registration successfully achieve inverse-consistency, but do not eliminate the regional bias that is the source of the error. In this work, we address the root of the problem: the non-uniformity of the cost function integral. We introduce a new quasi-volume-preserving constraint that allows for volume change only in areas with well-matching image intensities, and show that such a constraint puts a bound on the error arising from spatial non-uniformity. We demonstrate the advantages of adding the proposed constraint to standard (asymmetric and symmetrized) demons and diffeomorphic demons algorithms through experiments on synthetic images, and real X-ray and 2D/3D brain MRI data. Specifically, the results show that our approach leads to image alignment with more accurate matching of manually defined neuroanatomical structures, better tradeoff between image intensity matching and registration-induced distortion, improved native symmetry, and lower susceptibility to local optima. In summary, the inclusion of this space- and time-varying constraint leads to better image registration along every dimension that we have measured it.},
author = {Aganj, Iman and Reuter, Martin and Sabuncu, Mert R. and Fischl, Bruce},
doi = {10.1016/j.neuroimage.2014.10.059},
issn = {10959572},
journal = {NeuroImage},
keywords = {Deformable image registration,Integral non-uniformity,Inverse-consistency,Symmetry,Volume-preserving constraints},
pages = {238--251},
title = {{Avoiding symmetry-breaking spatial non-uniformity in deformable image registration via a quasi-volume-preserving constraint}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920130398{\&}doi=10.1016{\%}2Fj.neuroimage.2014.10.059{\&}partnerID=40{\&}md5=537fee2b2ea9e7af63a87f230be26ded},
volume = {106},
year = {2015}
}
@article{RN788,
abstract = {Mammography is the gold standard screening technique in breast cancer, but it has some limitations for women with dense breasts. In such cases, sonography is usually recommended as an additional imaging technique. A traditional sonogram produces a two-dimensional (2D) visualization of the breast and is highly operator dependent. Automated breast ultrasound (ABUS) has also been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency, facilitating double reading and comparison with past exams. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the three-dimensional (3D) nature of the images makes the analysis difficult and tedious for radiologists. The goal of this work is to develop a semi-automatic framework for breast lesion segmentation in ABUS volumes which is based on the Watershed algorithm. The effect of different de-noising methods on segmentation is studied showing a significant impact (p {\textless} 0.05) on the performance using a dataset of 28 temporal pairs resulting in a total of 56 ABUS volumes. The volumetric analysis is also used to evaluate the performance of the developed framework. A mean Dice Similarity Coefficient of 0.69 ± 0.11 with a mean False Positive ratio 0.35 ± 0.14 has been obtained. The Pearson correlation coefficient between the segmented volumes and the corresponding ground truth volumes is r2 = 0.960 (p = 0.05). Similar analysis, performed on 28 temporal (prior and current) pairs, resulted in a good correlation coefficient r2 = 0.967 (p {\textless} 0.05) for prior and r2 = 0.956 (p {\textless} 0.05) for current cases. The developed framework showed prospects to help radiologists to perform an assessment of ABUS lesion volumes, as well as to quantify volumetric changes during lesions diagnosis and follow-up.},
author = {Agarwal, Richa and Diaz, Oliver and Llad{\'{o}}, Xavier and Gubern-M{\'{e}}rida, Albert and Vilanova, Joan C. and Mart{\'{i}}, Robert},
doi = {10.1177/0161734617737733},
issn = {01617346},
journal = {Ultrasonic Imaging},
keywords = {ABUS (Automated Breast Ultrasound),breast cancer,lesion segmentation,temporal,volumetric analysis,watershed},
number = {2},
pages = {97--112},
title = {{Lesion Segmentation in Automated 3D Breast Ultrasound: Volumetric Analysis}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {40},
year = {2018}
}
@article{RN971,
abstract = {Medical image registration is the process of mapping two or more medical images into a single more informative image for the purpose of receiving precise and complementary information. The precise mapping of medical images obtained in different time-frames and by the same or different modalities is now possible due to the availability of large number of registration techniques. The purpose of this paper is to present and analyse intrinsic registration techniques for medical imaging in a comprehensive manner. Our approach of analysis is unique from already published work because we have performed detailed investigation on each registration techniques, and analyse similarity measures and assessments according to various parameters. The knowledge on the work that has been developed in the area is presented in a compact form. This work is expected to provide a useful platform for the researchers in the field of medical image registration in general and in intrinsic registration in particular.},
author = {Alam, Fakhre and Rahman, Sami Ur},
issn = {18119387},
journal = {Journal of Postgraduate Medical Institute},
keywords = {Image guided surgery,Intrinsic registration,Medical image registration,Medical imaging modalities},
number = {2},
pages = {119--132},
title = {{Intrinsic registration techniques for medical images: A state-of-the-art review}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964707329{\&}partnerID=40{\&}md5=77f7b0fdb11032a6d00501e3da414f03},
volume = {30},
year = {2016}
}
@article{RN830,
abstract = {Background: Whole-body diffusion weighted imaging (WB-DWI) has proven value to detect multiple myeloma (MM) lesions. However, the large volume of imaging data and the presence of numerous lesions makes the reading process challenging. The aim of the current study was to develop a semi-automatic lesion segmentation algorithm for WB-DWI images in MM patients and to evaluate this smart-algorithm (SA) performance by comparing it to the manual segmentations performed by radiologists. Methods: An atlas-based segmentation was developed to remove the high-signal intensity normal tissues on WB-DWI and to restrict the lesion area to the skeleton. Then, an outlier threshold-based segmentation was applied to WB-DWI images, and the segmented area's signal intensity was compared to the average signal intensity of a low-fat muscle on T1-weighted images. This method was validated in 22 whole-body DWI images of patients diagnosed with MM. Dice similarity coefficient (DSC), sensitivity and positive predictive value (PPV) were computed to evaluate the SA performance against the gold standard (GS) and to compare with the radiologists. A non-parametric Wilcoxon test was also performed. Apparent diffusion coefficient (ADC) histogram metrics and lesion volume were extracted for the GS segmentation and for the correctly identified lesions by SA and their correlation was assessed. Results: The mean inter-radiologists DSC was 0.323 ± 0.268. The SA vs GS achieved a DSC of 0.274 ± 0.227, sensitivity of 0.764 ± 0.276 and PPV 0.217 ± 0.207. Its distribution was not significantly different from the mean DSC of inter-radiologist segmentation (p = 0.108, Wilcoxon test). ADC and lesion volume intraclass correlation coefficient (ICC) of the GS and of the correctly identified lesions by the SA was 0.996 for the median and 0.894 for the lesion volume (p {\textless} 0.001). The duration of the lesion volume segmentation by the SA was, on average, 10.22 ± 0.86 min, per patient. Conclusions: The SA provides equally reproducible segmentation results when compared to the manual segmentation of radiologists. Thus, the proposed method offers robust and efficient segmentation of MM lesions on WB-DWI. This method may aid accurate assessment of tumor burden and therefore provide insights to treatment response assessment.},
author = {Almeida, S{\'{i}}lvia D. and Santinha, Joa{\~{o}} and Oliveira, Francisco P.M. and Ip, Joana and Lisitskaya, Maria and Louren{\c{c}}o, Joa{\~{o}} and Uysal, Aycan and Matos, Celso and Joa{\~{o}}, Cristina and Papanikolaou, Nikolaos},
doi = {10.1186/s40644-020-0286-5},
issn = {14707330},
journal = {Cancer Imaging},
keywords = {Atlas-based segmentation,Diffusion weighted imaging,Multiple myeloma,Semi-automatic segmentation,Total lesion burden},
number = {1},
title = {{Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077786257{\&}doi=10.1186{\%}2Fs40644-020-0286-5{\&}partnerID=40{\&}md5=c2b44a405ae7ba7bfa48478311ee70a6},
volume = {20},
year = {2020}
}
@inproceedings{RN969,
abstract = {The aim of this research is to compare some image segmentation methods for lungs based on performance evaluation parameter (Mean Square Error (MSE) and Peak Signal Noise to Ratio (PSNR)). In this study, the methods compared were connected threshold, neighborhood connected, and the threshold level set segmentation on the image of the lungs. These three methods require one important parameter, i.e the threshold. The threshold interval was obtained from the histogram of the original image. The software used to segment the image here was InsightToolkit-4.7.0 (ITK). This research used 5 lung images to be analyzed. Then, the results were compared using the performance evaluation parameter determined by using MATLAB. The segmentation method is said to have a good quality if it has the smallest MSE value and the highest PSNR. The results show that four sample images match the criteria of connected threshold, while one sample refers to the threshold level set segmentation. Therefore, it can be concluded that connected threshold method is better than the other two methods for these cases.},
author = {Amanda, A. R. and Widita, R.},
booktitle = {Journal of Physics: Conference Series},
doi = {10.1088/1742-6596/694/1/012048},
editor = {Haryanto, F and Ng, K H and Peralta, A P and Arimura, H and Viridi, S},
isbn = {17426588 (ISSN)},
issn = {17426596},
number = {1},
publisher = {Institute of Physics Publishing},
title = {{Comparison of image segmentation of lungs using methods: Connected threshold, neighborhood connected, and threshold level set segmentation}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971601490{\&}doi=10.1088{\%}2F1742-6596{\%}2F694{\%}2F1{\%}2F012048{\&}partnerID=40{\&}md5=211e94ed993c83699b57654dc8bcf38d},
volume = {694},
year = {2016}
}
@article{RN886,
abstract = {Objective: To assess the accuracy and precision of segmentation of the maxillary sinus in MR images to evaluate the potential usefulness of this modality in longitudinal studies of sinus development. Methods: A total of 15 healthy subjects who had been both craniofacial CT and MR scanned were included and the 30 maxillary sinus volumes were evaluated using segmentation. Two of the authors did segmentation of MRI and one of these authors did double segmentation. Agreement in results between CT and MRI as well as inter- and intraexaminer errors were evaluated by statistical and three-dimensional analysis. Results: The intraclass correlation coefficient for volume measurements for both method error, inter- and intraexaminer agreement were {\textgreater} 0.9 [maximal 95{\%} confidence interval of 0.989-0.997, p {\textless} 0.001] and the limit of agreement for all parameters were {\textless} 5.1{\%}. Segmentation errors were quantified in terms of overlap [Dice Coefficient (DICE) {\textgreater} 0.9 = excellent agreement] and border distance [95{\%} percentile Hausdorff Distance (HD) {\textless} 2 mm = acceptable agreement]. The results were replicable and not influenced by systematic errors. Conclusion: We found a high accuracy and precision of manual segmentation of the maxillary sinus in MR images. The largest mean errors were found close to the orbit and the teeth. Advances in knowledge: MRI can be used for 3D models of the paranasal sinuses with equally good results as CT and allows longitudinal follow-up of sinus development.},
author = {Andersen, Tobias N. and Darvann, Tron A. and Murakami, Shumei and Larsen, Per and Senda, Yurie and Bilde, Anders and Buchwald, Christian V. and Kreiborg, Sven},
doi = {10.1259/bjr.20170663},
issn = {00071285},
journal = {British Journal of Radiology},
number = {1085},
title = {{Accuracy and precision of manual segmentation of the maxillary sinus in MR images - A method study}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045980566{\&}doi=10.1259{\%}2Fbjr.20170663{\&}partnerID=40{\&}md5=fb09baba9be82130126233860365e1f2},
volume = {91},
year = {2018}
}
@inproceedings{RN847,
abstract = {Due to a lack of ubiquitous tools for volume data visualization, 3D rendering of volumetric content is shared and distributed as 2D media (video and static images). This work shows how using open web technologies (HTML5, JavaScript,WebGL and SVG), high quality volume rendering is achievable in an interactive manner with any WebGL-enabled device. In the web platform, real-time volume rendering algorithms are constrained to small datasets. This work presents a WebGL progressive ray-casting volume rendering approach that allows the interactive visualization of larger datasets with a higher rendering quality. This approach is better suited for devices with low compute capacity such as tablets and mobile devices. As a validation case, the presented method is used in an industrial quality inspection use case to visually assess the air void distribution of a plastic injection mould component in the web browser.},
author = {Arbelaiz, Ander and Moreno, Aitor and Barandiaran, I{\~{n}}igo and Garc{\'{i}}a-Alonso, Alejandro},
booktitle = {Proceedings - Web3D 2019: 24th International ACM Conference on 3D Web Technology},
doi = {10.1145/3329714.3338131},
editor = {Spencer, S N},
isbn = {9781450367981},
keywords = {Industrial application,Progressive rendering,Quality control,Ray-casting,Ubiquitous platforms,Void segmentation,Volume rendering,Web,WebGL},
publisher = {Association for Computing Machinery, Inc},
title = {{Progressive ray-casting volume rendering with WebGL for visual assessment of air void distribution in quality control}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071158969{\&}doi=10.1145{\%}2F3329714.3338131{\&}partnerID=40{\&}md5=2a6a9a2b43279419c898a8f0a38d80a6},
year = {2019}
}
@article{RN819,
abstract = {Many types of medical and scientific experiments acquire raw data in the form of images. Various forms of image processing and image analysis are used to transform the raw image data into quantitative measures that are the basis of subsequent statistical analysis. In this article we describe the SimpleITK R package. SimpleITK is a simplified interface to the insight segmentation and registration toolkit (ITK). ITK is an open source C++ toolkit that has been actively developed over the past 18 years and is widely used by the medical image analysis community. SimpleITK provides packages for many interpreter environments, including R. Currently, it includes several hundred classes for image analysis including a wide range of image input and output, filtering operations, and higher level components for segmentation and registration. Using SimpleITK, development of complex combinations of image and statistical analysis procedures is feasible. This article includes several examples of computational image analysis tasks implemented using SimpleITK, including spherical marker localization, multi-modal image registration, segmentation evaluation, and cell image analysis.},
author = {Beare, Richard and Lowekamp, Bradley and Yaniv, Ziv},
doi = {10.18637/jss.v086.i08},
issn = {15487660},
journal = {Journal of Statistical Software},
keywords = {Image processing,Image registration,Image segmentation,Medical imaging,R},
number = {8},
pages = {1--35},
title = {{Image segmentation, registration and characterization in R with simpleITK}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {86},
year = {2018}
}
@article{RN816,
abstract = {Accurate measurement of knee alignment, quantified by the hip-knee-ankle (HKA) angle (varus-valgus), serves as an essential biomarker in the diagnosis of various orthopaedic conditions and selection of appropriate therapies. Such angular deformities are assessed from standing X-ray panoramas. However, the limited field-of-view of traditional X-ray imaging systems necessitates the acquisition of several sector images to capture an individual's standing posture, and their subsequent ‘stitching' to reconstruct a panoramic image. Such panoramas are typically constructed manually by an X-ray imaging technician, often using various external markers attached to the individual's clothing and visible in two adjacent sector images. To eliminate human error, user-induced variability, improve consistency and reproducibility, and reduce the time associated with the traditional manual ‘stitching' protocol, here we propose an automatic panorama construction method that only relies on anatomical features reliably detected in the images, eliminating the need for any external markers or manual input from the technician. The method first performs a rough segmentation of the femur and the tibia, then the sector images are registered by evaluating a distance metric between the corresponding bones along their medial edge. The identified translations are then used to generate the standing panorama image. The method was evaluated on 95 patient image datasets from a database of X-ray images acquired across 10 clinical sites as part of the screening process for a multi-site clinical trial. The panorama reconstruction parameters yielded by the proposed method were compared to those used for the manual panorama construction, which served as gold-standard. The horizontal translation differences were 0:43 ± 1:95 mm 0:26 ± 1:43mm for the femur and tibia respectively, while the vertical translation differences were 3:76 ± 22:35 mm and 1:85 ± 6:79 mm for the femur and tibia, respectively. Our results showed no statistically significant differences between the HKA angles measured using the automated vs. the manually generated panoramas, and also led to similar decisions with regards to the patient inclusion/exclusion in the clinical trial. Thus, the proposed method was shown to provide comparable performance to manual panorama construction, with increased efficiency, consistency and robustness.},
author = {Ben-Zikri, Yehuda K. and Yaniv, Ziv R. and Baum, Karl and Linte, Cristian A.},
doi = {10.1080/21681163.2018.1537859},
issn = {21681171},
journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization},
keywords = {Long-limb X-ray,axial deformity at the knee,hip-knee-ankle angle,image registration,panorama reconstruction,segmentation},
number = {4},
pages = {464--478},
title = {{A marker-free registration method for standing X-ray panorama reconstruction for hip-knee-ankle axis deformity assessment}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {7},
year = {2019}
}
@inproceedings{RN912,
abstract = {A large diversity of image registration methodologies has emerged from the research community. The scattering of methods over toolboxes impedes rigorous comparison to select the appropriate method for a given application. Toolboxes typically tailor their implementations to a mathematical registration paradigm, which makes internal functionality nonexchangeable. Subsequently, this forms a barrier for adoption of registration technology in the clinic. We therefore propose a unifying, role-based software design that can integrate a broad range of functional registration components. These components can be configured into an algorithmic network via a single highlevel user interface. A generic component handshake mechanism provides users feedback on incompatibilities. We demonstrate the viability of our design by incorporating two paradigms from different code bases. The implementation is done in C++ and is available as open source. The progress of embedding more paradigms can be followed via https://github.com/kaspermarstal/SuperElastix.},
author = {Berendsen, Floris F. and Marstal, Kasper and Klein, Stefan and Staring, Marius},
booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
doi = {10.1109/CVPRW.2016.69},
isbn = {9781467388504},
issn = {21607516},
pages = {498--506},
publisher = {IEEE Computer Society},
title = {{The Design of SuperElastix - A Unifying Framework for a Wide Range of Image Registration Methodologies}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010223955{\&}doi=10.1109{\%}2FCVPRW.2016.69{\&}partnerID=40{\&}md5=dc7baf79ffe3f2bf7316f023fd6aa7a1},
year = {2016}
}
@article{RN921,
abstract = {Describing and analyzing heart multiphysics requires the acquisition and fusion of multisensor cardiac images. Multisensor image fusion enables a combined analysis of these heterogeneous modalities. We propose to register intra-patient multiview 2D+t ultrasound (US) images with multiview late gadolinium-enhanced (LGE) images acquired during cardiac magnetic resonance imaging (MRI), in order to fuse mechanical and tissue state information. The proposed procedure registers both US and LGE to cine MRI. The correction of slice misalignment and the rigid registration of multiview LGE and cine MRI are studied, to select the most appropriate similarity measure. It showed that mutual information performs the best for LGE slice misalignment correction and for LGE and cine registration. Concerning US registration, dynamic endocardial contours resulting from speckle tracking echocardiography were exploited in a geometry-based dynamic registration. We propose the use of an adapted dynamic time warping procedure to synchronize cardiac dynamics in multiview US and cine MRI. The registration of US and LGE MRI was evaluated on a dataset of patients with hypertrophic cardiomyopathy. A visual assessment of 330 left ventricular regions from US images of 28 patients resulted in 92.7{\%} of regions successfully aligned with cardiac structures in LGE. Successfully-aligned regions were then used to evaluate the abilities of strain indicators to predict the presence of fibrosis. Longitudinal peak-strain and peak-delay of aligned left ventricular regions were computed from corresponding regional strain curves from US. The Mann-Withney test proved that the expected values of these indicators change between the populations of regions with and without fibrosis (p {\textless} 0.01). ROC curves otherwise proved that the presence of fibrosis is one factor amongst others which modifies longitudinal peak-strain and peak-delay.},
author = {Betancur, Juli{\'{a}}n and Simon, Antoine and Halbert, Edgar and Tavard, Fran{\c{c}}ois and Carr{\'{e}}, Fran{\c{c}}ois and Hern{\'{a}}ndez, Alfredo and Donal, Erwan and Schnell, Fr{\'{e}}d{\'{e}}ric and Garreau, Mireille},
doi = {10.1016/j.media.2015.10.010},
issn = {13618423},
journal = {Medical Image Analysis},
keywords = {Cardiac ultrasound,Hypertrophic cardiomyopathy,Late gadolinium-enhanced magnetic resonance,Multimodal image registration,Speckle tracking echocardiography},
pages = {13--21},
title = {{Registration of dynamic multiview 2D ultrasound and late gadolinium enhanced images of the heart: Application to hypertrophic cardiomyopathy characterization}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948142529{\&}doi=10.1016{\%}2Fj.media.2015.10.010{\&}partnerID=40{\&}md5=7d93ff8e0d80ef9b1730c036b8529e1d},
volume = {28},
year = {2016}
}
@inproceedings{RN943,
abstract = {Voice hoarseness can have various reasons, one of them is a change of the vocal fold mucus. This change can be examined with micro endoscopes. Cell detection in these images is a difficult task, due to bad image quality, caused by noise and illumination variations. In previous works, it was observed that the repetitive pattern of the cell walls cause an elliptical shape in the Fourier domain [1, 2]. A manual segmentation and back transformation of this shape results in filtered images, where the cell detection is much easier [3]. The goal of this work is to automatically segment the elliptical shape in Fourier domain. Two different approaches are developed to get a suitable band-pass filter: a thresholding and an active contour method. After the band-pass filter is applied, the achieved results are superior to the manual segmentation case.},
author = {Bier, Bastian and Mualla, Firas and Steidl, Stefan and Bohr, Christopher and Neumann, Helmut and Maier, Andreas and Hornegger, Joachim},
booktitle = {Informatik aktuell},
doi = {10.1007/978-3-662-46224-9_71},
editor = {Deserno, T M and Tolxdorff, T and Handels, H and Meinzer, H P},
isbn = {9783662462232},
issn = {1431472X},
pages = {413--418},
publisher = {Kluwer Academic Publishers},
title = {{Band-pass filter design by segmentation in frequency domain for detection of epithelial cells in endomicroscope images}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85012231802{\&}doi=10.1007{\%}2F978-3-662-46224-9{\_}71{\&}partnerID=40{\&}md5=a2320e0e42ae26842cd9d13f26bc03b8},
volume = {0},
year = {2015}
}
@inproceedings{RN970,
abstract = {Image processing is important in diagnosing diseases or damages of human organs. One of the important stages of image processing is segmentation process. Segmentation is a separation process of the image into regions of certain similar characteristics. It is used to simplify the image to make an analysis easier. The case raised in this study is image segmentation of bones. Bone's image segmentation is a way to get bone dimensions, which is needed in order to make prosthesis that is used to treat broken or cracked bones. Segmentation methods chosen in this study are fast marching and geodesic active contours. This study uses ITK (Insight Segmentation and Registration Toolkit) software. The success of the segmentation was then determined by calculating its accuracy, sensitivity, and specificity. Based on the results, the Active Contours method has slightly higher accuracy and sensitivity values than the fast marching method. As for the value of specificity, fast marching has produced three image results that have higher specificity values compared to those of geodesic active contour's. The result also indicates that both methods have succeeded in performing bone's image segmentation. Overall, geodesic active contours method is quite better than fast marching in segmenting bone images.},
author = {Bilqis, A. and Widita, R.},
booktitle = {Journal of Physics: Conference Series},
doi = {10.1088/1742-6596/694/1/012044},
editor = {Haryanto, F and Ng, K H and Peralta, A P and Arimura, H and Viridi, S},
isbn = {17426588 (ISSN)},
issn = {17426596},
number = {1},
publisher = {Institute of Physics Publishing},
title = {{Comparison of segmentation using fast marching and geodesic active contours methods for bone}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971658619{\&}doi=10.1088{\%}2F1742-6596{\%}2F694{\%}2F1{\%}2F012044{\&}partnerID=40{\&}md5=5864b0c4fb7c08baa7e2a09f54dd0b9e},
volume = {694},
year = {2016}
}
@inproceedings{RN974,
abstract = {Human brain is a set of four communicating network of ventricles heaving with cerebrospinal fluid (CSF) which is located inside the brain parenchyma. An efficient segmentation of cerebral lateral ventricles one in each hemisphere can support the study of efficient pathologies for successful conclusion of various diseases. In this paper, an efficient and fast energy optimised technique for volumetric segmentation of lateral ventricles from MR images of human brain is proposed which is based on geodesic active contours using level set method. The proposed approach consists of mainly four main stages: 1. Preprocessing stage, 2. Presegmentation stage, 3. Contour Evolution with Energy optimisation stage, 4. Termination stage. Experiments on multislice MRI data obtained dice coefficient of 0.955, jaccard coefficient of 0.915 and other surface distance measures demonstrate the advantages of the proposed approach in both accuracy and efficiency.},
author = {Biswas, Ankur and Bhattacharya, P. and Maity, S. P.},
booktitle = {Procedia Computer Science},
doi = {10.1016/j.procs.2018.07.084},
editor = {Zoppi, M and Muthuswamy, S},
isbn = {18770509 (ISSN)},
issn = {18770509},
keywords = {Geodesic active contour,Lateral Ventricle,MR Images,Volumetric,level set methods},
pages = {561--568},
publisher = {Elsevier B.V.},
title = {{An efficient volumetric segmentation of cerebral lateral ventricles}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051328846{\&}doi=10.1016{\%}2Fj.procs.2018.07.084{\&}partnerID=40{\&}md5=e21c6be1fb5f5e12be609a506f661f8b},
volume = {133},
year = {2018}
}
@incollection{RN975,
abstract = {An efficient Segmentation of lateral ventricles plays a vital role in quantitatively analyzing the global and regional information in magnetic resonance imaging (MRI) of human brain. In this paper, a semi automatic segmentation methodology to support the study of efficient pathologies of the lateral ventricles along with white matter and gray matter of human brain is proposed. The segmentation is executed using an optimal geometric active contour with level set methods. A nominal anatomical knowledge is incorporated into the methodology in order to choose the most probable surfaces of the lateral ventricles of human brain, even if they are disconnected, and to eliminate addition of non ventricle cerebrospinal fluid (CSF) regions. The proposed segmentation method is applied to multislice MRI data and compared with region growing algorithms. The results Dice similarity coefficient 0.955, Jaccard similarity coefficient 0.815 demonstrates the reliability and efficiency.},
author = {Biswas, Ankur and Maity, Santi P. and Bhattacharya, Paritosh},
booktitle = {Communications in Computer and Information Science},
doi = {10.1007/978-981-13-1343-1_53},
editor = {Mandal, J K and Sinha, D},
isbn = {9789811313424},
issn = {18650929},
keywords = {Dice similarity coefficient,Geometric active contour,Jaccard similarity coefficient,Level set,Magnetic resonance imaging (MRI)},
pages = {646--657},
publisher = {Springer Verlag},
title = {{Optimal Geometric Active Contours: Application to Human Brain Segmentation}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053181661{\&}doi=10.1007{\%}2F978-981-13-1343-1{\_}53{\&}partnerID=40{\&}md5=65a97c1936282996fbe6ce8dfeb09631},
volume = {836},
year = {2018}
}
@incollection{RN968,
abstract = {In many scientific research fields, Matlab has been established as de facto tool for application design. This approach offers multiple advantages such as rapid deployment prototyping and the use of high performance linear algebra, among others. However, the applications developed are highly dependent of the Matlab runtime, limiting the deployment in heterogeneous platforms. In this paper we present the migration of a Matlab-implemented application to the C++ programming language, allowing the parallelization in GPUs. In particular, we have chosen RUMBA-SD, a spherical deconvolution algorithm, which estimates the intravoxel white-matter fiber orientations from diffusion MRI data. We describe the methodology used along with the tools and libraries leveraged during the translation task of such application. To demonstrate the benefits of the migration process, we perform a series of experiments using different high performance computing heterogeneous platforms and linear algebra libraries. This work aims to be a guide for future developments that are implemented out of Matlab. The results show that the C++ version attains, on average, a speedup of 8× over the Matlab one.},
author = {Blas, Javier Garcia and Dolz, Manuel F. and {Daniel Garcia}, J. and Carretero, Jesus and Daducci, Alessandro and Aleman, Yasser and Canales-Rodriguez, Erick Jorge},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-319-49583-5_49},
editor = {Carretero, J and Nakano, K and Ko, R K L and Mueller, P and Garcia-Blas, J},
isbn = {9783319495828},
issn = {16113349},
keywords = {Linear algebra,Magnetic resonance imaging,Matlab},
pages = {630--643},
publisher = {Springer Verlag},
title = {{Porting Matlab applications to high-performance C++ codes: CPU/GPU-accelerated spherical deconvolution of diffusion MRI data}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007236124{\&}doi=10.1007{\%}2F978-3-319-49583-5{\_}49{\&}partnerID=40{\&}md5=aa237e3a3fce5dcf7978db850e1b2615},
volume = {10048 LNCS},
year = {2016}
}
@article{RN832,
abstract = {Transparent research in musculoskeletal imaging is fundamental to reliably investigate diseases such as knee osteoarthritis (OA), a chronic disease impairing femoral knee cartilage. To study cartilage degeneration, researchers have developed algorithms to segment femoral knee cartilage from magnetic resonance (MR) images and to measure cartilage morphology and relaxometry. The majority of these algorithms are not publicly available or require advanced programming skills to be compiled and run. However, to accelerate discoveries and findings, it is crucial to have open and reproducible workflows. We present pyKNEEr, a framework for open and reproducible research on femoral knee cartilage from MR images. pyKNEEr is written in python, uses Jupyter notebook as a user interface, and is available on GitHub with a GNU GPLv3 license. It is composed of three modules: 1) image preprocessing to standardize spatial and intensity characteristics; 2) femoral knee cartilage segmentation for intersubject, multimodal, and longitudinal acquisitions; and 3) analysis of cartilage morphology and relaxometry. Each module contains one or more Jupyter notebooks with narrative, code, visualizations, and dependencies to reproduce computational environments. pyKNEEr facilitates transparent image-based research of femoral knee cartilage because of its ease of installation and use, and its versatility for publication and sharing among researchers. Finally, due to its modular structure, pyKNEEr favors code extension and algorithm comparison. We tested our reproducible workflows with experiments that also constitute an example of transparent research with pyKNEEr, and we compared pyKNEEr performances to existing algorithms in literature review visualizations. We provide links to executed notebooks and executable environments for immediate reproducibility of our findings.},
author = {Bonaretti, Serena and Gold, Garry E. and Beaupre, Gary S.},
doi = {10.1371/journal.pone.0226501},
issn = {19326203},
journal = {PLoS ONE},
number = {1},
title = {{PyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078319647{\&}doi=10.1371{\%}2Fjournal.pone.0226501{\&}partnerID=40{\&}md5=72a88df7ae12079c45940349b7f58af9},
volume = {15},
year = {2020}
}
@article{RN865,
abstract = {Purpose: To investigate a novel alternative diffusion-weighted imaging (DWI) approach using oscillating gradients preparation (OGSE) to obtain much shorter effective diffusion times ($\Delta$eff) for tumor response monitoring by apparent diffusion coefficient (ADC) mapping in a glioblastoma mouse model. Methods and Materials: Twenty-four BALB/c nude mice inoculated with U87 glioblastoma cells were randomized into a control group and an irradiation group, which underwent a 15-day fractioned radiation therapy (RT) course with 2 Gy/d. Therapy response was assessed by mapping of ADCs at 6 time points using an in-house implementation of a cos-OGSE DWI sequence with $\Delta$eff = 1.25 ms and compared with a standard pulsed gradient DWI protocol (PGSE) with typical clinical diffusion time $\Delta$eff = 18 ms. Longitudinal ADC changes in tumor and contralateral white matter (WM) were statistically assessed using repeated-measures analysis of variance and post hoc (Sidak) testing. Results: On short $\Delta$eff OGSE maps tumor ADC was generally 30{\%}-50{\%} higher than in surrounding WM. Areas correlated well with histology. Tumor identification was generally more difficult on PGSE maps owing to nonsignificant WM/tumor contrast. During RT, OGSE maps also showed significant tumor ADC increase (approximately 15{\%}) in response to radiation, consistently seen after 14-Gy RT dose. The clinical reference (PGSE) showed lower sensitivity to radiation changes, and no significant response across the radiation group and time course could be detected. Conclusion: Our short $\Delta$eff DWI method using OGSE better reflected histologically defined tumor areas and enabled more consistent and earlier detection of microstructural radiation changes than conventional methods. Oscillating gradients preparation offers significant potential as a robust microstructural RT response biomarker, potentially helping to shift important therapy decisions to earlier stages in the RT time course.},
author = {Bongers, Andre and Hau, Eric and Shen, Han},
doi = {10.1016/j.ijrobp.2017.12.280},
issn = {1879355X},
journal = {International Journal of Radiation Oncology Biology Physics},
number = {4},
pages = {1014--1023},
title = {{Short Diffusion Time Diffusion-Weighted Imaging With Oscillating Gradient Preparation as an Early Magnetic Resonance Imaging Biomarker for Radiation Therapy Response Monitoring in Glioblastoma: A Preclinical Feasibility Study}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041512823{\&}doi=10.1016{\%}2Fj.ijrobp.2017.12.280{\&}partnerID=40{\&}md5=acf090aa80c436bcbeed0e2695f55e04},
volume = {102},
year = {2018}
}
@incollection{RN959,
abstract = {Contrast-enhanced computed tomography angiography (CE-CTA) provides valuable, non-invasive assessment of lower extremity peripheral arterial disease (PAD). The advent of wide beam CT scanners has enabled multiple CT acquisitions over the same structure at a high frame rate, facilitating time-resolved CTA acquisitions. In this study, we investigate the technical feasibility of automatically quantifying the bolus arrival time and blood velocity in the arteries below the knee from time-resolved CTA. Our approach is based on arterial segmentation and local estimation of the bolus arrival time. The results are compared to values obtained through manual reading of the datasets and show good agreement. Based on a small patient study, we explore initial utility of these quantitative measures for the diagnosis of lower extremity PAD.},
author = {Boonen, Pieter Thomas and Buls, Nico and {Van Gompel}, Gert and {De Brucker}, Yannick and Aerden, Dimitri and {De Mey}, Johan and Vandemeulebroucke, Jef},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-030-01364-6_2},
editor = {Lee, S and Trucco, E and Maier-Hein, L and Moriconi, S and Albarqouni, S and Jannin, P and Balocco, S and Zahnd, G and Mateus, D and Taylor, Z and Demirci, S and Stoyanov, D and Sznitman, R and Martel, A and Cheplygina, V and Granger, E and Duong, L},
isbn = {9783030013639},
issn = {16113349},
keywords = {Artery segmentation,Blood velocity,Lower extremities,Peripheral arterial disease,Time-resolved CTA},
pages = {11--18},
publisher = {Springer Verlag},
title = {{Automated quantification of blood flow velocity from time-resolved CT angiography}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055830741{\&}doi=10.1007{\%}2F978-3-030-01364-6{\_}2{\&}partnerID=40{\&}md5=9917ad93753ce090a308f5318500b9e3},
volume = {11043 LNCS},
year = {2018}
}
@inproceedings{RN874,
abstract = {Signal inhomogeneity in MRI can influence significantly automatic data processing like segmentation, etc. or even affect the diagnostic procedure. In this work, a new method of intensity nonuniformity correction is presented. Our idea was to replace FCM clustering by k-harmonic means in the method that uses a standard N3 correction procedure. The algorithm was tested with MRI dataset acquired from a phantom object using a breast MRI coil to simulate real conditions during the study. Results were compared with five other methods using two indexes-integral uniformity and standard deviation of the signal inside the object. For the presented and improved method, the lowest integral uniformity and the reasonable low signal deviation were obtained.},
author = {Borys, Damian and Serafin, Wojciech and Frackiewicz, Mariusz and Psiuk-Maksymowicz, Krzysztof and Palus, Henryk},
booktitle = {Proceedings - 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018},
doi = {10.1109/SITIS.2018.00055},
editor = {Chbeir, R and di Baja, G S and Gallo, L and Yetongnon, K and Dipanda, A and Castrillon-Santana, M},
isbn = {9781538693858},
keywords = {Intensity nonuniformity correction,MRI,Phantom},
pages = {314--319},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{A Phantom Study of New Bias Field Correction Method Combining N3 and KHM for MRI Imaging}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065920171{\&}doi=10.1109{\%}2FSITIS.2018.00055{\&}partnerID=40{\&}md5=db972e30f4168923938969ac8921de9b},
year = {2018}
}
@article{RN845,
abstract = {Objective: To propose a hybrid multiatlas fusion and correction approach to estimate a pseudo–computed tomography (pCT) image from T2-weighted brain magnetic resonance (MR) images in the context of MRI-only radiotherapy. Materials and Methods: A set of eleven pairs of T2-weighted MR and CT brain images was included. Using leave-one-out cross-validation, atlas MR images were registered to the target MRI with multimetric, multiresolution deformable registration. The subsequent deformations were applied to the atlas CT images, producing uncorrected pCT images. Afterward, a three-dimensional hybrid CT number correction technique was used. This technique uses information about MR intensity, spatial location, and tissue label from segmented MR images with the fuzzy c-means algorithm and combines them in a weighted fashion to correct Hounsfield unit values of the uncorrected pCT images. The corrected pCT images were then fused into a final pCT image. Results: The proposed hybrid approach proved to be performant in correcting Hounsfield unit values in terms of qualitative and quantitative measures. Average correlation was 0.92 and 0.91 for the proposed approach by taking the mean and the median, respectively, compared with 0.86 for the uncorrected unfused version. Average values of dice similarity coefficient for bone were 0.68 and 0.72 for the fused corrected pCT images by taking the mean and the median, respectively, compared with 0.65 for the uncorrected unfused version indicating a significant bone estimation improvement. Conclusion: A hybrid fusion and correction method is presented to estimate a pCT image from T2-weighted brain MR images.},
author = {Boukellouz, Wafa and Moussaoui, Abdelouahab and Taleb-Ahmed, Abdelmalik and Boydev, Christine},
doi = {10.1016/j.jmir.2019.03.184},
issn = {18767982},
journal = {Journal of Medical Imaging and Radiation Sciences},
keywords = {MR-only radiotherapy,Pseudo-CT,brain,hybrid CT number correction,multiatlas fusion},
number = {3},
pages = {425--440},
title = {{Multiatlas Fusion with a Hybrid CT Number Correction Technique for Subject-Specific Pseudo-CT Estimation in the Context of MRI-Only Radiation Therapy}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065894779{\&}doi=10.1016{\%}2Fj.jmir.2019.03.184{\&}partnerID=40{\&}md5=710b20f7941f0fb981b12e9164a50e6a},
volume = {50},
year = {2019}
}
@article{RN797,
abstract = {Using magnetic resonance imaging (MRI) as the sole imaging modality for patient modeling in radiation therapy (RT) is a challenging task due to the need to derive electron density information from MRI and construct a so-called pseudo-computed tomography (pCT) image. We have previously published a new method to derive pCT images from head T1-weighted (T1-w) MR images using a single-atlas propagation scheme followed by a post hoc correction of the mapped CT numbers using local intensity information. The purpose of this study was to investigate the performance of our method with head zero echo time (ZTE) MR images. To evaluate results, the mean absolute error in bins of 20 HU was calculated with respect to the true planning CT scan of the patient. We demonstrated that applying our method using ZTE MR images instead of T1-w improved the correctness of the pCT in case of bone resection surgery prior to RT (that is, an example of large anatomical difference between the atlas and the patient).},
author = {Boydev, C. and Demol, B. and Pasquier, D. and Saint-Jalmes, H. and Delpon, G. and Reynaert, N.},
doi = {10.1016/j.ejmp.2017.04.028},
issn = {1724191X},
journal = {Physica Medica},
keywords = {Atlas-based method,Head cancer,MRI-only treatment planning,Pseudo-CT,ZTE sequence},
pages = {332--338},
title = {{Zero echo time MRI-only treatment planning for radiation therapy of brain tumors after resection}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {42},
year = {2017}
}
@article{RN848,
abstract = {Motion artefacts from involuntary changes in eye fixation remain a major imaging issue in optical coherence tomography (OCT). This paper reviews the state-of-the-art of retrospective procedures to correct retinal motion and axial eye motion artefacts in OCT imaging. Following an overview of motion induced artefacts and correction strategies, a chronological survey of retrospective approaches since the introduction of OCT until the current days is presented. Pre-processing, registration, and validation techniques are described. The review finishes by discussing the limitations of the current techniques and the challenges to be tackled in future developments.},
author = {Brea, Luisa S{\'{a}}nchez and {De Jesus}, Danilo Andrade and Shirazi, Muhammad Faizan and Pircher, Michael and van Walsum, Theo and Klein, Stefan},
doi = {10.3390/app9132700},
issn = {20763417},
journal = {Applied Sciences (Switzerland)},
keywords = {Image registration,Optical coherence tomography,Retinal motion artefacts},
number = {13},
title = {{Review on retrospective procedures to correct retinal motion artefacts in OCT imaging}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068857722{\&}doi=10.3390{\%}2Fapp9132700{\&}partnerID=40{\&}md5=609d603321731686ea1ae522250c7c6f},
volume = {9},
year = {2019}
}
@article{RN840,
abstract = {The alveolar capillary network (ACN) has a large surface area that provides the basis for an optimized gas exchange in the lung. It needs to adapt to morphological changes during early lung development and alveolarization. Structural alterations of the pulmonary vasculature can lead to pathological functional conditions such as in bronchopulmonary dysplasia and various other lung diseases. To understand the development of the ACN and its impact on the pathogenesis of lung diseases, methods are needed that enable comparative analyses of the complex three-dimensional structure of the ACN at different developmental stages and under pathological conditions. In this study a newborn mouse lung was imaged with serial block-face scanning electron microscopy (SBF-SEM) to investigate the ACN and its surrounding structures before the alveolarization process begins. Most parts but not all of the examined ACN contain two layers of capillaries, which were repeatedly connected with each other. A path from an arteriole to a venule was extracted and straightened to allow cross-sectional visualization of the data along the path within a plane. This allows a qualitative characterization of the structures that erythrocytes pass on their way through the ACN. One way to define regions of the ACN supplied by specific arterioles is presented and used for analyses. Pillars, possibly intussusceptive, were found in the vasculature but no specific pattern was observed in regard to parts of the saccular septa. This study provides 3D information with a resolution of about 150 nm on the microscopic structure of a newborn mouse lung and outlines some of the potentials and challenges of SBF-SEM for 3D analyses of the ACN.},
author = {Buchacker, Tobias and M{\"{u}}hlfeld, Christian and Wrede, Christoph and Wagner, Willi L. and Beare, Richard and McCormick, Matt and Grothausmann, Roman},
doi = {10.3389/fphys.2019.01357},
issn = {1664042X},
journal = {Frontiers in Physiology},
keywords = {3D reconstruction,capillary network,lung,segmentation,serial block-face scanning electron microscopy},
title = {{Assessment of the Alveolar Capillary Network in the Postnatal Mouse Lung in 3D Using Serial Block-Face Scanning Electron Microscopy}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076709753{\&}doi=10.3389{\%}2Ffphys.2019.01357{\&}partnerID=40{\&}md5=06fa9637c496f5d61d2f175943a787c0},
volume = {10},
year = {2019}
}
@article{RN850,
abstract = {Purpose: Proton CT (pCT) has the ability to reduce inherent uncertainties in proton treatment by directly measuring the relative proton stopping power with respect to water, thereby avoiding the uncertain conversion of X-ray CT Hounsfield unit to relative stopping power and the deleterious effect of X- ray CT artifacts. The purpose of this work was to further evaluate the potential of pCT for pretreatment positioning using experimental pCT data of a head phantom. Methods: The performance of a 3D image registration algorithm was tested with pCT reconstructions of a pediatric head phantom. A planning pCT simulation scan of the phantom was obtained with 200 MeV protons and reconstructed with a 3D filtered back projection (FBP) algorithm followed by iterative reconstruction and a representative pretreatment pCT scan was reconstructed with FBP only to save reconstruction time. The pretreatment pCT scan was rigidly transformed by prescribing random errors with six degrees of freedom or deformed by the deformation field derived from a head and neck cancer patient to the pretreatment pCT reconstruction, respectively. After applying the rigid or deformable image registration algorithm to retrieve the original pCT image before transformation, the accuracy of the registration was assessed. To simulate very low-dose imaging for patient setup, the proton CT images were reconstructed with 100{\%}, 50{\%}, 25{\%}, and 12.5{\%} of the total number of histories of the original planning pCT simulation scan, respectively. Results: The residual errors in image registration were lower than 1 mm and 1° of magnitude regardless of the anatomic directions and imaging dose. The mean residual errors ranges found for rigid image registration were from −0.29 ± 0.09 to 0.51 ± 0.50 mm for translations and from −0.05 ± 0.13 to 0.08 ± 0.08 degrees for rotations. The percentages of sub-millimetric errors found, for deformable image registration, were between 63.5{\%} and 100{\%}. Conclusion: This experimental head phantom study demonstrated the potential of low-dose pCT imaging for 3D image registration. Further work is needed to confirm the value pCT for pretreatment image-guided proton therapy.},
author = {Cassetta, Roberto and Piersimoni, Pierluigi and Riboldi, Marco and Giacometti, Valentina and Bashkirov, Vladmir and Baroni, Guido and Ordonez, Caesar and Coutrakon, George and Schulte, Reinhard},
doi = {10.1002/acm2.12565},
issn = {15269914},
journal = {Journal of Applied Clinical Medical Physics},
keywords = {deformable image registration,image reconstruction,proton CT,rigid image registration},
number = {4},
pages = {83--90},
title = {{Accuracy of low-dose proton CT image registration for pretreatment alignment verification in reference to planning proton CT}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063859861{\&}doi=10.1002{\%}2Facm2.12565{\&}partnerID=40{\&}md5=cf8e867163571978728a5b92c8fc3fab},
volume = {20},
year = {2019}
}
@inproceedings{RN883,
author = {{Cespedes Sanchez}, Pedro Pablo and {Legal Ayala}, Horacio and {Pinto Roa}, Diego and Gimenez, Gabriel Alberto and Lopez, Lorenzo and {Vazquez Noguera}, Jose Luis},
booktitle = {14th International Symposium on Medical Information Processing and Analysis, SIPAIM 2018},
doi = {10.1117/12.2506687},
editor = {Lepore, N and Romero, E and Brieva, J},
isbn = {9781510626058},
issn = {1996756X},
pages = {8},
publisher = {SPIE},
title = {{Intrapatient multimodal medical image registration of brain CT-MRI 3D: an approach based on metaheuristics}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060546679{\&}doi=10.1117{\%}2F12.2506687{\&}partnerID=40{\&}md5=5125dd9c739585e2fc9a96de92ac84ae},
volume = {10975},
year = {2018}
}
@article{RN885,
abstract = {This study proposes a regional fringe analysis (RFA) method to detect the regions of a target object in captured shifted images to improve depth measurement in phase-shifting fringe projection profilometry (PS-FPP). In the RFA method, region-based segmentation is exploited to segment the de-fringed image of a target object, and a multi-level fuzzy-based classification with five presented features is used to analyze and discriminate the regions of an object from the segmented regions, which were associated with explicit fringe information. Then, in the experiment, the performance of the proposed method is tested and evaluated on 26 test cases made of five types of materials. The qualitative and quantitative results demonstrate that the proposed RFA method can effectively detect the desired regions of an object to improve depth measurement in the PS-FPP system.},
author = {Chien, Kuang Che Chang and Tu, Han Yen and Hsieh, Ching Huang and Cheng, Chau Jern and Chang, Chun Yen},
doi = {10.1088/1361-6501/aa94a5},
issn = {13616501},
journal = {Measurement Science and Technology},
keywords = {Phase-shifting,classifcation,depth measurement,fringe projection proflometry,fuzzy analysis,region-based segmentation},
number = {1},
title = {{Regional fringe analysis for improving depth measurement in phase-shifting fringe projection profilometry}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039165127{\&}doi=10.1088{\%}2F1361-6501{\%}2Faa94a5{\&}partnerID=40{\&}md5=2a3721b607941509869568960082b198},
volume = {29},
year = {2018}
}
@article{RN916,
abstract = {Purpose: To develop an individually optimized contrast-enhanced (CE) 4D-computed tomography (CT) for radiotherapy simulation in pancreatic ductal adenocarcinomas (PDA). Methods: Ten PDA patients were enrolled. Each underwent three CT scans: a 4D-CT immediately following a CE 3D-CT and an individually optimized CE 4D-CT using test injection. Three physicians contoured the tumor and pancreatic tissues. Image quality scores, tumor volume, motion, tumor-to-pancreas contrast, and contrast-to-noise ratio (CNR) were compared in the three CTs. Interobserver variations were also evaluated in contouring the tumor using simultaneous truth and performance level estimation. Results: Average image quality scores for CE 3D-CT and CE 4D-CT were comparable (4.0 and 3.8, respectively; P = 0.082), and both were significantly better than that for 4D-CT (2.6, P {\textless} 0.001). Tumor-to-pancreas contrast results were comparable in CE 3D-CT and CE 4D-CT (15.5 and 16.7 Hounsfield units (HU), respectively; P = 0.21), and the latter was significantly higher than in 4D-CT (9.2 HU, P = 0.001). Image noise in CE 3D-CT (12.5 HU) was significantly lower than in CE 4D-CT (22.1 HU, P = 0.013) and 4D-CT (19.4 HU, P = 0.009). CNRs were comparable in CE 3D-CT and CE 4D-CT (1.4 and 0.8, respectively; P = 0.42), and both were significantly better in 4D-CT (0.6, P = 0.008 and 0.014). Mean tumor volumes were significantly smaller in CE 3D-CT (29.8 cm3, P = 0.03) and CE 4D-CT (22.8 cm3, P = 0.01) than in 4D-CT (42.0 cm3). Mean tumor motion was comparable in 4D-CT and CE 4D-CT (7.2 and 6.2 mm, P = 0.17). Interobserver variations were comparable in CE 3D-CT and CE 4D-CT (Jaccard index 66.0{\%} and 61.9{\%}, respectively) and were worse for 4D-CT (55.6{\%}) than CE 3D-CT. Conclusions: CE 4D-CT demonstrated characteristics comparable to CE 3D-CT, with high potential for simultaneously delineating the tumor and quantifying tumor motion with a single scan.},
author = {Choi, Wookjin and Xue, Ming and Lane, Barton F. and Kang, Min Kyu and Patel, Kruti and Regine, William F. and Klahr, Paul and Wang, Jiahui and Chen, Shifeng and D'Souza, Warren and Lu, Wei},
doi = {10.1118/1.4963213},
issn = {00942405},
journal = {Medical Physics},
keywords = {4D-CT,contrast enhancement,pancreatic ductal adenocarcinoma,radiotherapy simulation},
number = {10},
pages = {5659--5666},
title = {{Individually optimized contrast-enhanced 4D-CT for radiotherapy simulation in pancreatic ductal adenocarcinoma}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988808490{\&}doi=10.1118{\%}2F1.4963213{\&}partnerID=40{\&}md5=d555238a514a92d5a318ee19a0427dba},
volume = {43},
year = {2016}
}
@article{RN843,
abstract = {Cone beam computed tomography (CBCT) is a medical imaging technique employed for diagnosis and treatment of patients with cranio-maxillofacial deformities. CBCT 3D reconstruction and segmentation of bones such as mandible or maxilla are essential procedures in surgical and orthodontic treatments. However, CBCT image processing may be impaired by features such as low contrast, inhomogeneity, noise and artifacts. Besides, values assigned to voxels are relative Hounsfield units unlike traditional computed tomography (CT). Such drawbacks render CBCT segmentation a difficult and time-consuming task, usually performed manually with tools designed for medical image processing. We present an interactive two-stage method for the segmentation of CBCT: (i) we first perform an automatic segmentation of bone structures with super-voxels, allowing a compact graph representation of the 3D data; (ii) next, a user-placed seed process guides a graph partitioning algorithm, splitting the extracted bones into mandible and skull. We have evaluated our segmentation method in three different scenarios and compared the results with ground truth data of the mandible and the skull. Results show that our method produces accurate segmentation and is robust to changes in parameters. We also compared our method with two similar segmentation strategy and showed that it produces more accurate segmentation. Finally, we evaluated our method for CT data of patients with deformed or missing bones and the segmentation was accurate for all data. The segmentation of a typical CBCT takes in average 5 min, which is faster than most techniques currently available.},
author = {{Cuadros Linares}, Oscar and Bianchi, Jonas and Raveli, Dirceu and {Batista Neto}, Jo{\~{a}}o and Hamann, Bernd},
doi = {10.1007/s00371-018-1511-0},
issn = {01782789},
journal = {Visual Computer},
keywords = {Bone segmentation,Cone beam computed tomography,Graph clustering,Mandible,Skull,Super-voxels},
number = {10},
pages = {1461--1474},
title = {{Mandible and skull segmentation in cone beam computed tomography using super-voxels and graph clustering}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045951942{\&}doi=10.1007{\%}2Fs00371-018-1511-0{\&}partnerID=40{\&}md5=a294525f068f2ad564fa94a0fcd267bf},
volume = {35},
year = {2019}
}
@article{RN875,
abstract = {Introduction: Linear registration to a standard space is one of the major steps in processing and analyzing magnetic resonance images (MRIs) of the brain. Here we present an overview of linear stereotaxic MRI registration and compare the performance of 5 publicly available and extensively used linear registration techniques in medical image analysis. Methods: A set of 9693 T1-weighted MR images were obtained for testing from 4 datasets: ADNI, PREVENT-AD, PPMI, and HCP, two of which have multi-center and multi-scanner data and three of which have longitudinal data. Each individual native image was linearly registered to the MNI ICBM152 average template using five versions of MRITOTAL from MINC tools, FLIRT from FSL, two versions of Elastix, spm{\_}affreg from SPM, and ANTs linear registration techniques. Quality control (QC) images were generated from the registered volumes and viewed by an expert rater to assess the quality of the registrations. The QC image contained 60 sub-images (20 of each of axial, sagittal, and coronal views at different levels throughout the brain) overlaid with contours of the ICBM152 template, enabling the expert rater to label the registration as acceptable or unacceptable. The performance of the registration techniques was then compared across different datasets. In addition, the effect of image noise, intensity non-uniformity, age, head size, and atrophy on the performance of the techniques was investigated by comparing differences between age, scaling factor, ventricle volume, brain volume, and white matter hyperintensity (WMH) volumes between passed and failed cases for each method. Results: The average registration failure rate among all datasets was 27.41{\%}, 27.14{\%}, 12.74{\%}, 13.03{\%}, 0.44{\%} for the five versions of MRITOTAL techniques, 8.87{\%} for ANTs, 11.11{\%} for FSL, 12.35{\%} for Elastix Affine, 24.40{\%} for Elastix Similarity, and 30.66{\%} for SPM. There were significant effects of signal to noise ratio, image intensity non-uniformity estimates, as well as age, head size, and atrophy related changes between passed and failed registrations. Conclusion: Our experiments show that the Revised BestLinReg had the best performance among the evaluated registration techniques while all techniques performed worse for images with higher levels of noise and non-uniformity as well as atrophy related changes.},
author = {Dadar, Mahsa and Fonov, Vladimir S. and Collins, D. Louis},
doi = {10.1016/j.neuroimage.2018.03.025},
issn = {10959572},
journal = {NeuroImage},
keywords = {Linear registration,MRI,Quality control},
pages = {191--200},
title = {{A comparison of publicly available linear MRI stereotaxic registration techniques}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044150830{\&}doi=10.1016{\%}2Fj.neuroimage.2018.03.025{\&}partnerID=40{\&}md5=a707203bafd861c84c210833597b8ad5},
volume = {174},
year = {2018}
}
@inproceedings{RN908,
abstract = {{\textcopyright} 2017 SPIE. Accurate tumor segmentation is a critical step in the development of the computer-aided detection (CAD) based quantitative image analysis scheme for early stage prognostic evaluation of ovarian cancer patients. The purpose of this investigation is to assess the efficacy of several different methods to segment the metastatic tumors occurred in different organs of ovarian cancer patients. In this study, we developed a segmentation scheme consisting of eight different algorithms, which can be divided into three groups: 1) Region growth based methods; 2) Canny operator based methods; and 3) Partial differential equation (PDE) based methods. A number of 138 tumors acquired from 30 ovarian cancer patients were used to test the performance of these eight segmentation algorithms. The results demonstrate each of the tested tumors can be successfully segmented by at least one of the eight algorithms without the manual boundary correction. Furthermore, modified region growth, classical Canny detector, and fast marching, and threshold level set algorithms are suggested in the future development of the ovarian cancer related CAD schemes. This study may provide meaningful reference for developing novel quantitative image feature analysis scheme to more accurately predict the response of ovarian cancer patients to the chemotherapy at early stage.},
author = {Danala, Gopichandh and Wang, Yunzhi and Thai, Theresa and Gunderson, Camille C. and Moxley, Katherine M. and Moore, Kathleen and Mannel, Robert S. and Cheng, Samuel and Liu, Hong and Zheng, Bin and Qiu, Yuchen},
booktitle = {Biophotonics and Immune Responses XII},
doi = {10.1117/12.2250978},
editor = {Chen, W R},
isbn = {9781510605718},
issn = {16057422},
pages = {100650J},
publisher = {SPIE},
title = {{Improving efficacy of metastatic tumor segmentation to facilitate early prediction of ovarian cancer patients' response to chemotherapy}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019246953{\&}doi=10.1117{\%}2F12.2250978{\&}partnerID=40{\&}md5=d74ac0c887f79b14d956f34ba1490c94},
volume = {10065},
year = {2017}
}
@article{RN833,
abstract = {Purpose: Cardiac image segmentation is a critical process for generating personalized models of the heart and for quantifying cardiac performance parameters. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV), and the myocardium from cardiac cine MR images is challenging due to variability of the normal and abnormal anatomy, as well as the imaging protocols. This study proposes a multi-task learning (MTL)-based regularization of a convolutional neural network (CNN) to obtain accurate segmenation of the cardiac structures from cine MR images. Methods: We train a CNN network to perform the main task of semantic segmentation, along with the simultaneous, auxiliary task of pixel-wise distance map regression. The network also predicts uncertainties associated with both tasks, such that their losses are weighted by the inverse of their corresponding uncertainties. As a result, during training, the task featuring a higher uncertainty is weighted less and vice versa. The proposed distance map regularizer is a decoder network added to the bottleneck layer of an existing CNN architecture, facilitating the network to learn robust global features. The regularizer block is removed after training, so that the original number of network parameters does not change. The trained network outputs per-pixel segmentation when a new patient cine MR image is provided as an input. Results: We show that the proposed regularization method improves both binary and multi-class segmentation performance over the corresponding state-of-the-art CNN architectures. The evaluation was conducted on two publicly available cardiac cine MRI datasets, yielding average Dice coefficients of 0.84 ± 0.03 and 0.91 ± 0.04. We also demonstrate improved generalization performance of the distance map regularized network on cross-dataset segmentation, showing as much as 42{\%} improvement in myocardium Dice coefficient from 0.56 ± 0.28 to 0.80 ± 0.14. Conclusions: We have presented a method for accurate segmentation of cardiac structures from cine MR images. Our experiments verify that the proposed method exceeds the segmentation performance of three existing state-of-the-art methods. Furthermore, several cardiac indices that often serve as diagnostic biomarkers, specifically blood pool volume, myocardial mass, and ejection fraction, computed using our method are better correlated with the indices computed from the reference, ground truth segmentation. Hence, the proposed method has the potential to become a non-invasive screening and diagnostic tool for the clinical assessment of various cardiac conditions, as well as a reliable aid for generating patient specific models of the cardiac anatomy for therapy planning, simulation, and guidance.},
archivePrefix = {arXiv},
arxivId = {1901.01238},
author = {Dangi, Shusil and Linte, Cristian A. and Yaniv, Ziv},
doi = {10.1002/mp.13853},
eprint = {1901.01238},
issn = {00942405},
journal = {Medical Physics},
keywords = {cardiac segmentation,convolutional neural network,magnetic resonance imaging,multi-task learning,regularization,task uncertainty weighting},
number = {12},
pages = {5637--5651},
title = {{A distance map regularized CNN for cardiac cine MR image segmentation}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074595228{\&}doi=10.1002{\%}2Fmp.13853{\&}partnerID=40{\&}md5=e49d5b643280b5d657eba92082261710},
volume = {46},
year = {2019}
}
@incollection{RN838,
abstract = {Segmentation of the left ventricle and quantification of various cardiac contractile functions is crucial for the timely diagnosis and treatment of cardiovascular diseases. Traditionally, the two tasks have been tackled independently. Here we propose a convolutional neural network based multi-task learning approach to perform both tasks simultaneously, such that, the network learns better representation of the data with improved generalization performance. Probabilistic formulation of the problem enables learning the task uncertainties during the training, which are used to automatically compute the weights for the tasks. We performed a five fold cross-validation of the myocardium segmentation obtained from the proposed multi-task network on 97 patient 4-dimensional cardiac cine-MRI datasets available through the STACOM LV segmentation challenge against the provided gold-standard myocardium segmentation, obtaining a Dice overlap of (Formula Presented) and mean surface distance of (Formula Presented) mm, while simultaneously estimating the myocardial area with mean absolute difference error of (Formula Presented).},
archivePrefix = {arXiv},
arxivId = {1809.10221},
author = {Dangi, Shusil and Yaniv, Ziv and Linte, Cristian A.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-030-12029-0_3},
editor = {Li, S and McLeod, K and Young, A and Rhode, K and Pop, M and Zhao, J and Sermesant, M and Mansi, T},
eprint = {1809.10221},
isbn = {9783030120283},
issn = {16113349},
pages = {21--31},
publisher = {Springer Verlag},
title = {{Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064043381{\&}doi=10.1007{\%}2F978-3-030-12029-0{\_}3{\&}partnerID=40{\&}md5=89d8de887e5c7b9bb24cf49448d058fe},
volume = {11395 LNCS},
year = {2019}
}
@article{RN966,
abstract = {In this study, we present several image segmentation techniques for various image scales and modalities. We consider cellular-, organ-, and whole organism-levels of biological structures in cardiovascular applications. Several automatic segmentation techniques are presented and discussed in this work. The overall pipeline for reconstruction of biological structures consists of the following steps: image pre-processing, feature detection, initial mask generation, mask processing, and segmentation post-processing. Several examples of image segmentation are presented, including patient-specific abdominal tissues segmentation, vascular network identification and myocyte lipid droplet micro-structure reconstruction.},
author = {Danilov, Alexander and Pryamonosov, Roman and Yurova, Alexandra},
doi = {10.3390/computation4030035},
issn = {20793197},
journal = {Computation},
keywords = {Abdominal tissues,Cardiovascular applications,Cerebral arteries,Coronary arteries,Electron microscopy,Image segmentation},
number = {3},
title = {{Image segmentation for cardiovascular biomedical applications at different scales}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050390538{\&}doi=10.3390{\%}2Fcomputation4030035{\&}partnerID=40{\&}md5=ec04fcf1cbad06c8db55480c5b77767f},
volume = {4},
year = {2016}
}
@article{RN941,
abstract = {Current osteoporosis treatments improve bone mass by increasing net bone formation: anti-resorptive drugs such as bisphosphonates block osteoclast activity, while anabolic agents such as parathyroid hormone (PTH) increase bone remodeling, with a greater effect on formation. Although these drugs are widely used, their role in modulating formation and resorption is not fully understood, due in part to technical limitations in the ability to longitudinally assess bone remodeling. Importantly, it is not known whether or not PTH-induced bone formation is independent of resorption, resulting in controversy over the effectiveness of combination therapies that use both PTH and an anti-resorptive. In this study, we developed a $\mu$CT-based, in vivo dynamic bone histomorphometry technique for rat tibiae, and applied this method to longitudinally track changes in bone resorption and formation as a result of treatment with alendronate (ALN), PTH, or combination therapy of both PTH and ALN (PTH+ALN). Correlations between our $\mu$CT-based measures of bone formation and measures of bone formation based on calcein-labeled histology (r = 0.72-0.83) confirm the accuracy of this method. Bone remodeling parameters measured through $\mu$CT-based in vivo dynamic bone histomorphometry indicate an increased rate of bone formation in rats treated with PTH and PTH+ALN, together with a decrease in bone resorption measures in rats treated with ALN and PTH+ALN. These results were further supported by traditional histology-based measurements, suggesting that PTH was able to induce bone formation while bone resorption was suppressed.},
author = {de Bakker, Chantal M.J. and Altman, Allison R. and Tseng, Wei Ju and Tribble, Mary Beth and Li, Connie and Chandra, Abhishek and Qin, Ling and Liu, X. Sherry},
doi = {10.1016/j.bone.2014.12.061},
issn = {87563282},
journal = {Bone},
keywords = {Animal models/rodent,Anti-resorptive treatment,Bone stiffness,In vivo $\mu$CT,Parathyroid hormone,Trabecular bone microstructure},
pages = {198--207},
title = {{$\mu$CT-based, in vivo dynamic bone histomorphometry allows 3D evaluation of the early responses of bone resorption and formation to PTH and alendronate combination therapy}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920973040{\&}doi=10.1016{\%}2Fj.bone.2014.12.061{\&}partnerID=40{\&}md5=ce16efe68357b4c7c5a21d428c6efe79},
volume = {73},
year = {2015}
}
@article{RN903,
abstract = {Pregnancy, lactation, and weaning result in dramatic changes in maternal calcium metabolism. In particular, the increased calcium demand during lactation causes a substantial degree of maternal bone loss. This reproductive bone loss has been suggested to be largely reversible, as multiple clinical studies have found that parity and lactation history have no adverse effect on postmenopausal fracture risk. However, the precise effects of pregnancy, lactation, and post-weaning recovery on maternal bone structure are not well understood. Our study aimed to address this question by longitudinally tracking changes in trabecular and cortical bone microarchitecture at the proximal tibia in rats throughout three cycles of pregnancy, lactation, and post-weaning using in vivo $\mu$CT. We found that the trabecular thickness underwent a reversible deterioration during pregnancy and lactation, which was fully recovered after weaning, whereas other parameters of trabecular microarchitecture (including trabecular number, spacing, connectivity density, and structure model index) underwent a more permanent deterioration, which recovered minimally. Thus, pregnancy and lactation resulted in both transient and long-lasting alterations in trabecular microstructure. In the meantime, multiple reproductive cycles appeared to improve the robustness of cortical bone (resulting in an elevated cortical area and polar moment of inertia), as well as increase the proportion of the total load carried by the cortical bone at the proximal tibia. Taken together, changes in the cortical and trabecular compartments suggest that whereas rat tibial trabecular bone appears to be highly involved in maintaining calcium homeostasis during female reproduction, cortical bone adapts to increase its load-bearing capacity, allowing the overall mechanical function of the tibia to be maintained. {\textcopyright} 2017 American Society for Bone and Mineral Research.},
author = {de Bakker, Chantal M.J. and Altman-Singles, Allison R. and Li, Yihan and Tseng, Wei Ju and Li, Connie and Liu, X. Sherry},
doi = {10.1002/jbmr.3084},
issn = {15234681},
journal = {Journal of Bone and Mineral Research},
keywords = {BONE FORMATION,BONE MICROARCHITECTURE,LACTATION,PREGNANCY,STIFFNESS,WEANING},
number = {5},
pages = {1014--1026},
title = {{Adaptations in the Microarchitecture and Load Distribution of Maternal Cortical and Trabecular Bone in Response to Multiple Reproductive Cycles in Rats}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013042443{\&}doi=10.1002{\%}2Fjbmr.3084{\&}partnerID=40{\&}md5=18906d1bf5b2d3d0e94d19b1af8384bf},
volume = {32},
year = {2017}
}
@inproceedings{RN936,
abstract = {Planning safe trajectories in keyhole neurosurgery requires a high level of accuracy in order to access to small structures either by biopsies, stimulating deep brain and others. We propose a computer system that carries out decision making based on rules using fuzzy logic to plan safe trajectories for preoperative neurosurgery. The processes to generate input values of membership functions, and implementation of the system for decision function will be explained. The results of risk weights for each candidate trajectory are evaluated and the safest calculated trajectories taking into account the risk structures that there are in the brain from the insertion points to the target point are visualized.},
author = {{De Le{\'{o}}n Cuevas}, Alejandro and Tovar-Arriaga, Sa{\'{u}}l and Gonz{\'{a}}lez-Guti{\'{e}}rrez, Arturo and Aceves-Fern{\'{a}}ndez, Marco Antonio},
booktitle = {2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2015},
doi = {10.1109/ICEEE.2015.7357927},
isbn = {9781467378390},
keywords = {Image guided surgery,Trajectory planning,artificial intelligence in surgery,fuzzy logic},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Trajectory planning for keyhole neurosurgery using fuzzy logic for risk evaluation}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962882000{\&}doi=10.1109{\%}2FICEEE.2015.7357927{\&}partnerID=40{\&}md5=2ec0aab6774734025966c01139e1fb7a},
year = {2015}
}
@inproceedings{RN837,
abstract = {Spectral imaging is a ubiquitous tool in modern biochemistry. Despite acquiring dozens to thousands of spectral channels, existing technology cannot capture spectral images at the same spatial resolution as structural microscopy. Due to partial voluming and low light exposure, spectral images are often difficult to interpret and analyze. This highlights a need to upsample the low-resolution spectral image by using spatial information contained in the high-resolution image, thereby creating a fused representation with high specificity both spatially and spectrally. In this paper, we propose a framework for the fusion of co-registered structural and spectral microscopy images to create super-resolved representations of spectral images. As a first application, we super-resolve spectral images of ex-vivo retinal tissue imaged with confocal laser scanning microscopy, by using spatial information from structured illumination microscopy. Second, we super-resolve mass spectroscopic images of mouse brain tissue, by using spatial information from high-resolution histology images. We present a systematic validation of model assumptions crucial towards maintaining the original nature of spectra and the applicability of super-resolution. Goodness-of-fit for spectral predictions are evaluated through functional R2 values, and the spatial quality of the super-resolved images are evaluated using normalized mutual information. {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.},
author = {Dey, Neel and Li, Shijie and Bermond, Katharina and Heintzmann, Rainer and Curcio, Christine A. and Ach, Thomas and Gerig, Guido},
booktitle = {Medical Imaging 2019: Image Processing},
doi = {10.1117/12.2512598},
editor = {Angelini, E D and Angelini, E D and Angelini, E D and Landman, B A},
isbn = {9781510625457},
issn = {0277-786X},
pages = {12},
publisher = {SPIE},
title = {{Multi-modal image fusion for multispectral super-resolution in microscopy}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068325681{\&}doi=10.1117{\%}2F12.2512598{\&}partnerID=40{\&}md5=bb0775b42af9f68ef3a088e55974d94e},
volume = {10949},
year = {2019}
}
@article{RN814,
abstract = {Purpose: To develop a methodological framework to simultaneously measure R2* and magnetic susceptibility in trabecularized yellow bone marrow and to investigate the sensitivity of Quantitative Susceptibility Mapping (QSM) for measuring trabecular bone density using a non-UTE multi-gradient echo sequence. Methods: The ankle of 16 healthy volunteers and two patients was scanned using a time-interleaved multi-gradient-echo (TIMGRE) sequence. After field mapping based on water–fat separation methods and background field removal based on the Laplacian boundary value method, three different QSM dipole inversion schemes were implemented. Mean susceptibility values in regions of different trabecular bone density in the calcaneus were compared to the corresponding values in the R2* maps, bone volume to total volume ratios (BV/TV) estimated from high resolution imaging (in 14 subjects), and CT attenuation (in two subjects). In addition, numerical simulations were performed in a simplified trabecular bone model of randomly positioned spherical bone inclusions to verify and compare the scaling of R2* and susceptibility with BV/TV. Results: Differences in calcaneus trabecularization were well depicted in susceptibility maps, in good agreement with high-resolution MR and CT images. Simulations and in vivo scans showed a linear relationship of measured susceptibility with BV/TV and R2*. The ankle in vivo results showed a strong linear correlation between susceptibility and R2* (R2 = 0.88, p {\textless} 0.001) with a slope and intercept of −0.004 and 0.2 ppm, respectively. Conclusions: A method for multi-paramteric mapping, including R2* -mapping and QSM was developed for measuring trabecularized yellow bone marrow, showing good sensitivity of QSM for measuring trabecular bone density.},
author = {Diefenbach, Maximilian N. and Meineke, Jakob and Ruschke, Stefan and Baum, Thomas and Gersing, Alexandra and Karampinos, Dimitrios C.},
doi = {10.1002/mrm.27531},
issn = {15222594},
journal = {Magnetic Resonance in Medicine},
keywords = {susceptibility mapping,trabecular bone density},
number = {3},
pages = {1739--1754},
title = {{On the sensitivity of quantitative susceptibility mapping for measuring trabecular bone density}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {81},
year = {2019}
}
@inproceedings{RN926,
author = {Dmitriev, Konstantin and Gutenko, Ievgeniia and Nadeem, Saad and Kaufman, Arie},
booktitle = {Medical Imaging 2016: Image Processing},
doi = {10.1117/12.2216537},
editor = {Styner, M A and Angelini, E D and Angelini, E D},
isbn = {9781510600195},
issn = {16057422},
pages = {97842C},
publisher = {SPIE},
title = {{Pancreas and cyst segmentation}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981736248{\&}doi=10.1117{\%}2F12.2216537{\&}partnerID=40{\&}md5=0c6892196eefd59bd4552f6498d124be},
volume = {9784},
year = {2016}
}
@article{RN931,
abstract = {The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD) plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD) is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs) to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG) optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU).},
author = {Du, Xiaogang and Dang, Jianwu and Wang, Yangping and Wang, Song and Lei, Tao},
doi = {10.1155/2016/7419307},
issn = {17486718},
journal = {Computational and Mathematical Methods in Medicine},
title = {{A Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85008957755{\&}doi=10.1155{\%}2F2016{\%}2F7419307{\&}partnerID=40{\&}md5=94c05725af9454145efeff48e39a8b1e},
volume = {2016},
year = {2016}
}
@article{RN905,
abstract = {The use of medical imaging in diagnosing brain disease is growing. The challenges are related to the big size of data and complexity of the image processing. High standard of hardware and software are demanded, which can only be provided in big hospitals. Our purpose was to provide a smart cloud system to help diagnosing brain diseases for hospital with limited infrastructure. The expertise of neurologists was first implanted in cloud server to conduct an automatic diagnosis in real time using image processing technique developed based on ITK library and web service. Users upload images through website and the result, in this case the size of tumor was sent back immediately. A specific image compression technique was developed for this purpose. The smart cloud system was able to measure the area and location of tumors, with average size of 19.91 ± 2.38 cm2 and an average response time 7.0 ± 0.3 s. The capability of the server decreased when multiple clients accessed the system simultaneously: 14 ± 0 s (5 parallel clients) and 27 ± 0.2 s (10 parallel clients). The cloud system was successfully developed to process and analyze medical images for diagnosing brain diseases in this case for tumor.},
author = {Fahmi, Fahmi and Nasution, Tigor H. and Anggreiny},
doi = {10.3233/THC-171298},
issn = {09287329},
journal = {Technology and Health Care},
keywords = {Cloud system,ITK,brain diseases,medical image,web services},
number = {3},
pages = {607--610},
title = {{Smart cloud system with image processing server in diagnosing brain diseases dedicated for hospitals with limited resources}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020190896{\&}doi=10.3233{\%}2FTHC-171298{\&}partnerID=40{\&}md5=43aa37ad080ba10e71273ea68294d0c5},
volume = {25},
year = {2017}
}
@inproceedings{RN963,
abstract = {{\textcopyright} 2017 SPIE. A 3D kinematic measurement of joint movement is crucial for orthopedic surgery assessment and diagnosis. This is usually obtained through a frame-by-frame registration of the 3D bone volume to a uoroscopy video of the joint movement. The high cost of a high-quality uoroscopy imaging system has hindered the access of many labs to this application. This is while the more affordable and low-dosage version, the mini C-Arm, is not commonly used for this application due to low image quality. In this paper, we introduce a novel method for kinematic analysis of joint movement using the mini C-Arm. In this method the bone of interest is recovered and isolated from the rest of the image using a non-rigid registration of an atlas to each frame. The 3D/2D registration is then performed using the weighted histogram of image gradients as an image feature. In our experiments, the registration error was 0.89 mm and 2.36°for human C2 vertebra. While the precision is still lacking behind a high quality uoroscopy machine, it is a good starting point facilitating the use of mini C-Arms for motion analysis making this application available to lower-budget environments. Moreover, the registration was highly resistant to the initial distance from the true registration, converging to the answer from anywhere within ±90°of it.},
author = {Ghafurian, Soheil and Hacihaliloglu, Ilker and Metaxas, Dimitris N. and Tan, Virak and Li, Kang},
booktitle = {Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling},
doi = {10.1117/12.2254678},
editor = {Webster, R J and Fei, B},
isbn = {9781510607156},
issn = {16057422},
pages = {101350B},
publisher = {SPIE},
title = {{3D/2D image registration method for joint motion analysis using low-quality images from mini C-arm machines}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020389194{\&}doi=10.1117{\%}2F12.2254678{\&}partnerID=40{\&}md5=6fd25bdbdca508f33b2b3846f0f0d7fc},
volume = {10135},
year = {2017}
}
@inproceedings{RN929,
abstract = {{\textcopyright} 2016 SPIE.Digitally reconstructed radiographs (DRR) are a simulation of radiographic images produced through a perspective projection of the three-dimensional (3D) image (volume) onto a two-dimensional (2D) image plane. The traditional method for the generation of DRRs, namely ray-casting, is a computationally intensive process and accounts for most of solution time in 3D/2D medical image registration frameworks, where a large number of DRRs is required. A few alternate methods for a faster DRR generation have been proposed, the most successful of which are based on the idea of pre-calculating the attenuation value of possible rays. Despite achieving good quality, these methods support a limited range of motion for the volume and entail long pre-calculation time. In this paper, we propose a new preprocessing procedure and data structure for the calculation of the ray attenuation values. This method supports all possible volume positions with practically small memory requirements in addition to reducing the complexity of the problem from O(n3) to O(n2). In our experiments, we generated DRRs of high quality in 63 milliseconds with a preprocessing time of 99.48 seconds and a memory size of 7.45 megabytes.},
author = {Ghafurian, Soheil and Metaxas, Dimitris N. and Tan, Virak and Li, Kang},
booktitle = {Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling},
doi = {10.1117/12.2217756},
editor = {Webster, R J and Yaniv, Z R},
isbn = {9781510600218},
issn = {16057422},
pages = {97860C},
publisher = {SPIE},
title = {{Fast generation of digitally reconstructed radiograph through an efficient preprocessing of ray attenuation values}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982108538{\&}doi=10.1117{\%}2F12.2217756{\&}partnerID=40{\&}md5=dc30594f8f1ae1c399aff0783e094425},
volume = {9786},
year = {2016}
}
@article{RN891,
abstract = {Objectives: To assess the role in predicting nipple-areola complex (NAC) involvement of a newly developed automatic method which computes the 3D tumor-NAC distance. Patients and Methods: Ninety-nine patients scheduled to nipple sparing mastectomy (NSM) underwent magnetic resonance (MR) examination at 1.5 T, including sagittal T2w and dynamic contrast enhanced (DCE)-MR imaging. An automatic method was developed to segment the NAC and the tumor and to compute the 3D distance between them. The automatic measurement was compared with manual axial and sagittal 2D measurements. NAC involvement was defined by the presence of invasive ductal or lobular carcinoma and/or ductal carcinoma in situ or ductal intraepithelial neoplasia (DIN1c − DIN3). Results: Tumor-NAC distance was computed on 95/99 patients (25 NAC+), as three tumors were not correctly segmented (sensitivity = 97{\%}), and 1 NAC was not detected (sensitivity = 99{\%}). The automatic 3D distance reached the highest area under the receiver operating characteristic (ROC) curve (0.830) with respect to the manual axial (0.676), sagittal (0.664), and minimum distances (0.664). At the best cut-off point of 21 mm, the 3D distance obtained sensitivity = 72{\%}, specificity = 80{\%}, positive predictive value = 56{\%}, and negative predictive value = 89{\%}. Conclusions: This method could provide a reproducible biomarker to preoperatively select breast cancer patients candidates to NSM, thus helping surgical planning and intraoperative management of patients.},
author = {Giannini, Valentina and Bianchi, Veronica and Carabalona, Silvia and Mazzetti, Simone and Maggiorotto, Furio and Kubatzki, Franziska and Regge, Daniele and Ponzone, Riccardo and Martincich, Laura},
doi = {10.1002/jso.24788},
issn = {10969098},
journal = {Journal of Surgical Oncology},
keywords = {3D automatic distance,breast cancer,magnetic resonance imaging,mastectomy,nipple-areola sparing,tumor segmentation},
number = {8},
pages = {1069--1078},
title = {{MRI to predict nipple-areola complex (NAC) involvement: An automatic method to compute the 3D distance between the NAC and tumor}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040316780{\&}doi=10.1002{\%}2Fjso.24788{\&}partnerID=40{\&}md5=759b8bc9fcaf202c7bea3274c812f4c2},
volume = {116},
year = {2017}
}
@article{RN852,
abstract = {Purpose: Pathological complete response (pCR) following neoadjuvant chemoradiotherapy or radiotherapy in locally advanced rectal cancer (LARC) is reached in approximately 15–30{\%} of cases, therefore it would be useful to assess if pretreatment of 18 F-FDG PET/CT and/or MRI texture features can reliably predict response to neoadjuvant therapy in LARC. Methods: Fifty-two patients were dichotomized as responder (pR+) or non-responder (pR-) according to their pathological tumor regression grade (TRG) as follows: 22 as pR+ (nine with TRG = 1, 13 with TRG = 2) and 30 as pR- (16 with TRG = 3, 13 with TRG = 4 and 1 with TRG = 5). First-order parameters and 21 second-order texture parameters derived from the Gray-Level Co-Occurrence matrix were extracted from semi-automatically segmented tumors on T2w MRI, ADC maps, and PET/CT acquisitions. The role of each texture feature in predicting pR+ was assessed with monoparametric and multiparametric models. Results: In the mono-parametric approach, PET homogeneity reached the maximum AUC (0.77; sensitivity = 72.7{\%} and specificity = 76.7{\%}), while PET glycolytic volume and ADC dissimilarity reached the highest sensitivity (both 90.9{\%}). In the multiparametric analysis, a logistic regression model containing six second-order texture features (five from PET and one from T2w MRI) yields the highest predictivity in distinguish between pR+ and pR- patients (AUC = 0.86; sensitivity = 86{\%}, and specificity = 83{\%} at the Youden index). Conclusions: If preliminary results of this study are confirmed, pretreatment PET and MRI could be useful to personalize patient treatment, e.g., avoiding toxicity of neoadjuvant therapy in patients predicted pR-.},
author = {Giannini, V. and Mazzetti, S. and Bertotto, I. and Chiarenza, C. and Cauda, S. and Delmastro, E. and Bracco, C. and {Di Dia}, A. and Leone, F. and Medico, E. and Pisacane, A. and Ribero, D. and Stasi, M. and Regge, D.},
doi = {10.1007/s00259-018-4250-6},
issn = {16197089},
journal = {European Journal of Nuclear Medicine and Molecular Imaging},
keywords = {18 F-FDG PET/CT imaging,Locally advanced rectal cancer,Magnetic resonance imaging,Prediction of treatment response,Radiomics,Texture features},
number = {4},
pages = {878--888},
title = {{Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18 F-FDG PET and MRI radiomics features}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060031543{\&}doi=10.1007{\%}2Fs00259-018-4250-6{\&}partnerID=40{\&}md5=4bf80d097b9dd71dfebe905f5b3b38d8},
volume = {46},
year = {2019}
}
@article{RN911,
abstract = {Objective: To assess whether a computer-aided, diagnosis (CAD) system can predict pathological Complete Response (pCR) to neoadjuvant chemotherapy (NAC) prior to treatment using texture features. Methods: Response to treatment of 44 patients was defined according to the histopatology of resected tumour and extracted axillary nodes in two ways: (a) pCR+ (Smith's Grade = 5) vs pCR- (Smith's Grade {\textless} 5); (b) pCRN+ (pCR+ and absence of residual lymph node metastases) vs pCRN-. A CAD system was developed to: (i) segment the breasts; (ii) register the DCE-MRI sequence; (iii) detect the lesion and (iv) extract 27 3D texture features. The role of individual texture features, multiparametric models and Bayesian classifiers in predicting patients' response to NAC were evaluated. Results: A cross-validated Bayesian classifier fed with 6 features was able to predict pCR with a specificity of 72{\%} and a sensitivity of 67{\%}. Conversely, 2 features were used by the Bayesian classifier to predict pCRN, obtaining a sensitivity of 69{\%} and a specificity of 61{\%}. Conclusion: A CAD scheme, that extracts texture features from an automatically segmented 3D mask of the tumour, could predict pathological response to NAC. Additional research should be performed to validate these promising results on a larger cohort of patients and using different classification strategies. Advances in knowledge: This is the first study assessing the role of an automatic CAD system in predicting the pathological response to NAC before treatment. Fully automatic methods represent the backbone of standardized analysis and may help in timely managing patients candidate to NAC.},
author = {Giannini, Valentina and Mazzetti, Simone and Marmo, Agnese and Montemurro, Filippo and Regge, Daniele and Martincich, Laura},
doi = {10.1259/bjr.20170269},
issn = {00071285},
journal = {British Journal of Radiology},
number = {1077},
title = {{A computer-aided diagnosis (CAD) scheme for pretreatment prediction of pathological response to neoadjuvant therapy using dynamic contrast-enhanced MRI texture features}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028072747{\&}doi=10.1259{\%}2Fbjr.20170269{\&}partnerID=40{\&}md5=5c537a3d7909e31a3bd3d481046791ce},
volume = {90},
year = {2017}
}
@article{RN939,
abstract = {Multiparametric (mp)-Magnetic Resonance Imaging (MRI) is emerging as a powerful test to diagnose and stage prostate cancer (PCa). However, its interpretation is a time consuming and complex feat requiring dedicated radiologists. Computer-aided diagnosis (CAD) tools could allow better integration of data deriving from the different MRI sequences in order to obtain accurate, reproducible, non-operator dependent information useful to identify and stage PCa. In this paper, we present a fully automatic CAD system conceived as a 2-stage process. First, a malignancy probability map for all voxels within the prostate is created. Then, a candidate segmentation step is performed to highlight suspected areas, thus evaluating both the sensitivity and the number of false positive (FP) regions detected by the system. Training and testing of the CAD scheme is performed using whole-mount histological sections as the reference standard. On a cohort of 56 patients (i.e. 65 lesions) the area under the ROC curve obtained during the voxel-wise step was 0.91, while, in the second step, a per-patient sensitivity of 97{\%} was reached, with a median number of FP equal to 3 in the whole prostate. The system here proposed could be potentially used as first or second reader to manage patients suspected to have PCa, thus reducing both the radiologist's reporting time and the inter-reader variability. As an innovative setup, it could also be used to help the radiologist in setting the MRI-guided biopsy target.},
author = {Giannini, Valentina and Mazzetti, Simone and Vignati, Anna and Russo, Filippo and Bollito, Enrico and Porpiglia, Francesco and Stasi, Michele and Regge, Daniele},
doi = {10.1016/j.compmedimag.2015.09.001},
issn = {18790771},
journal = {Computerized Medical Imaging and Graphics},
keywords = {Computer aided detection,Image analysis,Multiparametric MRI,Prostate cancer,SVM classifier},
pages = {219--226},
title = {{A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983127840{\&}doi=10.1016{\%}2Fj.compmedimag.2015.09.001{\&}partnerID=40{\&}md5=3a43e32746c5917a989d8eb7f9942bb2},
volume = {46},
year = {2015}
}
@article{RN878,
abstract = {Keyhole surgeries become increasingly important in clinical daily routine as they help minimizing the damage of a patient's healthy tissue. The planning of keyhole surgeries is based on medical imaging and an important factor that influences the surgeries' success. Due to the image reconstruction process, medical image data contains uncertainty that exacerbates the planning of a keyhole surgery. In this paper we present a visual workflow that helps clinicians to examine and compare different surgery paths as well as visualizing the patients' affected tissue. The analysis is based on the concept of hierarchical image semantics, that segment the underlying image data with respect to the input images' uncertainty and the users understanding of tissue composition. Users can define arbitrary surgery paths that they need to investigate further. The defined paths can be queried by a rating function to identify paths that fulfill user-defined properties. The workflow allows a visual inspection of the affected tissues and its substructures. Therefore, the workflow includes a linked view system indicating the three-dimensional location of selected surgery paths as well as how these paths affect the patients tissue. To show the effectiveness of the presented approach, we applied it to the planning of a keyhole surgery of a brain tumor removal and a kneecap surgery.},
author = {Gillmann, Christina and Maack, Robin G.C. and Post, Tobias and Wischgoll, Thomas and Hagen, Hans},
doi = {10.1016/j.visinf.2018.04.004},
issn = {2468502X},
journal = {Visual Informatics},
keywords = {Keyhole Surgery Planning,Uncertainty Visualization,Visual Analytics Workflow},
number = {1},
pages = {26--36},
title = {{An Uncertainty-aware Workflow for Keyhole Surgery Planning using Hierarchical Image Semantics}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066739022{\&}doi=10.1016{\%}2Fj.visinf.2018.04.004{\&}partnerID=40{\&}md5=373c953331ead7b18b936c470ef6d581},
volume = {2},
year = {2018}
}
@article{RN868,
abstract = {Surface extraction is an important step in the image processing pipeline to estimate the size and shape of an object. Unfortunately, state of the art surface extraction algorithms form a straight forward extraction based on a pre-defined value that can lead to surfaces, that are not accurate. Furthermore, most isosurface extraction algorithms lack the ability to communicate uncertainty originating from the image data. This can lead to a rejection of such algorithms in many applications. To solve this problem, we propose a methodology to extract and optimize surfaces from image data based on a defined uncertainty model. To identify optimal parameters, the presented method defines a parameter space that is evaluated and rates each extraction run based on the remaining surface uncertainty. The resulting surfaces can be explored intuitively in an interactive framework. We applied our methodology to a variety of datasets to demonstrate the quality of the resulting surfaces.},
author = {Gillmann, Christina and Wischgoll, Thomas and Hamann, Bernd and Hagen, Hans},
doi = {10.1016/j.gmod.2018.07.004},
issn = {15240703},
journal = {Graphical Models},
keywords = {Parameter space exploration,Surface extraction,Uncertainty visualization},
pages = {13--21},
title = {{Accurate and reliable extraction of surfaces from image data using a multi-dimensional uncertainty model}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051412806{\&}doi=10.1016{\%}2Fj.gmod.2018.07.004{\&}partnerID=40{\&}md5=5d10dfa161a616c746fa9eae0597d362},
volume = {99},
year = {2018}
}
@article{RN786,
abstract = {Purpose: To describe a new system for scanned ion beam therapy, named RIDOS (Real-time Ion DOse planning and delivery System), which performs real time delivered dose verification integrating the information from a clinical beam monitoring system with a Graphic Processing Unit (GPU) based dose calculation in patient Computed Tomography. Methods: A benchmarked dose computation algorithm for scanned ion beams has been parallelized and adapted to run on a GPU architecture. A workstation equipped with a NVIDIA GPU has been interfaced through a National Instruments PXI-crate with the dose delivery system of the Italian National Center of Oncological Hadrontherapy (CNAO) to receive in real-time the measured beam parameters. Data from a patient monitoring system are also collected to associate the respiratory phases with each spot during the delivery of the dose. Using both measured and planned spot properties, RIDOS evaluates during the few seconds of inter-spill time the cumulative delivered and prescribed dose distributions and compares them through a fast $\gamma$-index algorithm. Results: The accuracy of the GPU-based algorithms was assessed against the CPU-based ones and the differences were found below 1‰. The cumulative planned and delivered doses are computed at the end of each spill in about 300 ms, while the dose comparison takes approximatively 400 ms. The whole operation provides the results before the next spill starts. Conclusions: RIDOS system is able to provide a fast computation of the delivered dose in the inter-spill time of the CNAO facility and allows to monitor online the dose deposition accuracy all along the treatment.},
author = {Giordanengo, S. and Vignati, A. and Attili, A. and Ciocca, M. and Donetti, M. and Fausti, F. and Manganaro, L. and Milian, F. M. and Molinelli, S. and Monaco, V. and Russo, G. and Sacchi, R. and {Varasteh Anvar}, M. and Cirio, R.},
doi = {10.1016/j.ejmp.2019.03.029},
issn = {1724191X},
journal = {Physica Medica},
keywords = {Dose delivery,GPU,Online dose computation,Pencil beam scanning},
pages = {139--149},
title = {{RIDOS: A new system for online computation of the delivered dose distributions in scanning ion beam therapy}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {60},
year = {2019}
}
@article{RN957,
abstract = {Objectives: To evaluate the accuracy of ITK-SNAP software for measuring volumes of a non-regular shape structure, using cone beam computed tomography (CBCT) scans, besides for developing a mathematical model to correct the software measurement error in case it existed. Methods: A phantom made by moulding a rubber duck's head was filled with total (38,000 mm3) and partial volumes of water (7000 mm3,

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