--- license: cc-by-4.0 task_categories: - image-segmentation tags: - medical - ct - pelvis - prostate - radiotherapy - organs-at-risk - rtstruct - dicom - tcia - autosegmentation - edge-cases pretty_name: Prostate Anatomical Edge Cases (pelvic OAR CT) size_categories: - n<1K dataset_info: features: - name: patient_id dtype: string - name: ct_series_uid dtype: string - name: rt_series_uid dtype: string - name: num_ct_slices dtype: int32 - name: slice_index dtype: int32 - name: n_structures dtype: int32 - name: missing_structures dtype: string - name: prostate_voxels dtype: int64 - name: rectum_voxels dtype: int64 - name: bladder_voxels dtype: int64 - name: femur_head_l_voxels dtype: int64 - name: femur_head_r_voxels dtype: int64 - name: image dtype: image - name: mask dtype: image - name: overlay dtype: image splits: - name: preview num_bytes: 28895143 num_examples: 131 download_size: 28894404 dataset_size: 28895143 configs: - config_name: default data_files: - split: preview path: data/preview-* --- # Prostate-Anatomical-Edge-Cases **Stress-Testing Pelvic Autosegmentation Algorithms Using Anatomical Edge Cases** — a TCIA collection of **pelvic radiotherapy planning CT** with manually contoured organs at risk, curated so that most cases contain anatomy known to break autosegmentation algorithms (Kanwar et al., *Phys Imaging Radiat Oncol* 2023). > **Read before using — the name is misleading in two ways:** > 1. **This is CT, not MRI.** Despite "Prostate" in the name it is *not* a prostate > mpMRI/zonal dataset: there is no T2W/DWI/ADC, no peripheral or transition > zone, no urethra, and **no lesion labels**. It is RT simulation CT with > pelvic organ-at-risk contours, and 4 of the 5 classes are not the prostate. > 2. **"Edge cases" means difficult *anatomy*, not difficult lesions.** The cohort > was selected for hip arthroplasty hardware, prostatic median-lobe hypertrophy, > "droopy" seminal vesicles, Foley catheters, SpaceOAR hydrogel, brachytherapy > seeds/calcifications, extracapsular surface irregularity, narrow rectum, > in-field bowel and morbid obesity — features that degrade atlas-, model- and > deep-learning-based contouring. ## Dataset Details | Field | Value | |---|---| | Modality | CT (RT simulation) + DICOM RTSTRUCT contours | | Body part | Pelvis — prostate and surrounding organs at risk | | Task | 3D multi-organ segmentation | | Patients | 131 (112 anatomical edge cases + 19 normal controls) | | Series | 262 = 131 CT + 131 RTSTRUCT (1:1 paired) | | CT slices | 23,359 (min 86 / median 167 / max 379 per case) | | Scanner | Philips Brilliance Big Bore (100%); RTSTRUCT exported from Varian ARIA RTM | | Format | DICOM (CT) + DICOM RTSTRUCT (contours) | | Size | ~17 GB | | License | CC BY 4.0 | | DOI | `10.7937/QSTF-ST65` | | Source | Oregon Health & Science University (single institution) | There is **no train/val/test split** — this is a stress-test cohort, not a challenge. Splitting is left to the consumer. ## Classes Five foreground structures, present in the `StructureSetROISequence` of every case, named per TG-263 and all typed `ORGAN`: | Label | ROI name | |---|---| | 0 | background | | 1 | `Prostate` | | 2 | `Rectum` | | 3 | `Bladder` | | 4 | `Femur_Head_L` | | 5 | `Femur_Head_R` | ## Ground Truth — single manual tier Every ROI in every case carries `ROIGenerationAlgorithm = MANUAL`. The contours were drawn by a single radiation oncologist, peer-reviewed, and used clinically for treatment planning — so there is exactly one annotation tier and no rater ambiguity. The three autosegmentation outputs benchmarked in the paper (atlas-based/MIM, model-based/RayStation, deep-learning U-Net/RayStation v9B) were **not** deposited; all 262 series report `ThirdPartyAnalysis = NO`. The RTSTRUCTs here are the gold standard, not algorithm output. ## Important Notes for Loaders These were verified by parsing all 131 RTSTRUCT objects and are easy to get wrong: - **15 of 131 cases declare an ROI with zero contours.** The ROI appears in `StructureSetROISequence` but its `ContourSequence` is empty, so naive code silently emits an all-background mask for that class instead of skipping it. Only **116/131 have all five structures non-empty**: | Empty structure | n | Patient IDs (`Prostate-AEC-…`) | |---|---|---| | `Rectum` | 7 | 009, 116, 117, 119, 125, 127, 134 | | `Femur_Head_L` | 4 | 020, 037, 106, 111 | | `Femur_Head_R` | 3 | 010, 012, 027 | | `Prostate` | 1 | 072 | | `Bladder` | 0 | — (always present) | - **`Prostate-AEC-101` carries a sixth ROI, `BODY`** (external contour). Build masks by matching ROI **name**, never by ROI index or order. - **Patient IDs are non-contiguous**: `Prostate-AEC-001` … `Prostate-AEC-134` with **021, 032 and 043 absent** → 131 patients. - **`ApprovalStatus = UNAPPROVED` on all 131.** This is a de-identification re-export artifact and contradicts the paper's "clinically approved" statement — do **not** use it as a quality filter. - **`Prostate-AEC-072` is doubly suspect**: empty `Prostate` plus a `Rectum` whose contours integrate to an anatomically implausible volume. Consider excluding it. - **Dates are shifted** (`StudyDate` reads 1992–2003 for a 2011–2019 cohort) and `PatientIdentityRemoved = YES`. - **Rasterisation**: each RTSTRUCT references exactly one CT series via `ReferencedFrameOfReferenceSequence`, so contour→labelmap conversion with `rt_utils` (or manual polygon filling against the CT `ImagePositionPatient` grid) is unambiguous. ## Cohort labels (edge case vs control) are NOT in this release The paper's Data Availability statement says "edge case labels and basic demographic data have been deposited on TCIA", but **they were not**. TCIA exposes only the manifest and the NBIA digest; the RTSTRUCT headers carry no edge-case tag, no `ClinicalTrialSubjectID` and no `PatientComments`. The 112-vs-19 edge/control assignment and the 8 anatomical-variant classes (prostate hypertrophy 52, droopy seminal vesicles 37, hip arthroplasty 11, surface irregularity 9, calcifications/seeds 8, Foley catheter 4, SpaceOAR 4, narrow rectum 2, in-field bowel 1, morbid obesity 1 — overlapping, so multi-label) exist **only in the paper's Supplementary Table 2**. That table also numbers cases 1–131 contiguously while TCIA uses 001–134 with gaps, so the join is not the identity map. This mirror deliberately ships **only what TCIA publishes** rather than a derived mapping. ## Cross-dataset Overlap **None known.** Every widely-used prostate segmentation set is MRI from a different institution, whereas this is CT from OHSU: PROMISE12, PI-CAI, QIN-PROSTATE, Prostate-3T, PROSTATEx, NCI-ISBI 2013, MSD Task05_Prostate, ProstateX-Seg-HiRes, Prostate-MRI-US-Biopsy and Prostate Fused-MRI-Pathology all share neither modality nor cohort. TCIA reports "No related Collections found", and there is no cross-reference ID field — `Prostate-AEC-###` IDs are collection-local. ## Structure ``` images///*.dcm # CT (131 series, 23,359 slices) segmentations///*.dcm # RTSTRUCT (131 objects, 1 per patient) series_to_patient.json # series-level metadata (all 262) LICENSE.txt ``` `series_to_patient.json` keys each `SeriesInstanceUID` to: `PatientID`, `Collection`, `StudyInstanceUID`, `Modality`, `SeriesDescription`, `SeriesNumber`, `BodyPartExamined`, `Manufacturer`, `ManufacturerModelName`, `ImageCount`, `FileSize`, `License`, `DOI`, `ThirdPartyAnalysis`, and the relative `path` — so CT↔RTSTRUCT pairing needs no TCIA round-trip. ## Source & Citation - TCIA collection: https://www.cancerimagingarchive.net/collection/prostate-anatomical-edge-cases/ - DOI: `10.7937/QSTF-ST65` - Official, author-deposited (Thompson, Kanwar, Merz, Cohen, Fisher, Rana, Claunch, Hung); fully public, no registration required. ```bibtex @article{kanwar2023edgecases, author = {Kanwar, Aasheesh and Merz, Brandon and Claunch, Cheryl and Rana, Shushan and Hung, Arthur and Thompson, Reid F.}, title = {Stress-testing pelvic autosegmentation algorithms using anatomical edge cases}, journal = {Physics and Imaging in Radiation Oncology}, volume = {25}, pages = {100413}, year = {2023}, doi = {10.1016/j.phro.2023.100413} } @misc{thompson2023paec, author = {Thompson, R. F. and Kanwar, A. and Merz, B. and Cohen, E. and Fisher, H. and Rana, S. and Claunch, C. and Hung, A.}, title = {Stress-Testing Pelvic Autosegmentation Algorithms Using Anatomical Edge Cases [Data set]}, year = {2023}, publisher = {The Cancer Imaging Archive}, doi = {10.7937/QSTF-ST65} } @article{clark2013tcia, author = {Clark, Kenneth and Vendt, Bruce and Smith, Kirk and others}, title = {The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository}, journal = {Journal of Digital Imaging}, volume = {26}, number = {6}, pages = {1045--1057}, year = {2013}, doi = {10.1007/s10278-013-9622-7} } ```