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  • Stress-Testing Pelvic Autosegmentation Algorithms Using Anatomical Edge Cases (Prostate-Anatomical-Edge-Cases)

Summary

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In this single institution retrospective study, we reviewed 950 consecutive patients with prostate adenocarcinoma receiving definitive radiotherapy between 2011 and 2019, and identified among them 112 patients with anatomic variations (edge cases) seen on simulation CT and/or MRI imaging. These variations included hip arthroplasty, prostate median lobe hypertrophy, so-called “droopy” seminal vesicles, presence of a urinary catheter, and others. A separate cohort of 19 “normal” cases were randomly selected for inclusion. Prostate, rectum, bladder, and bilateral femoral heads were manually segmented on all CT simulation images (where present) and were ultimately used clinically for radiation treatment planning.

We leveraged this imaging dataset to assess the comparative performance of deep learning, atlas-based, and model-based autosegmentation methods across both normal and edge case cohorts: https://doi.org/10.1016/j.phro.2023.100413.  In this paper and in the figure on the right, we show the Cross-sectional CT-based anatomy and autosegmentation performance for representative edge cases.

A) Hypertrophic prostate edge case. Each panel depicts a focused excerpt from a single CT scan, centered about two different structures (prostate, bladder) in three different planes (axial, sagittal, coronal). Clinician-delineated “ground truth” contours (MD) for each structure are shown in red, while atlas-based (AB), model-based (MB), and deep-learning based (DL) autosegmented contours are depicted in green, orange, and blue, respectively. Numerical values represent DSC for the corresponding autosegmented volumes compared to MD volumes.

B) So-called “droopy” seminal vesicles edge case. Each panel depicts a focused excerpt from a single CT scan, centered about the prostate in two different planes (axial, sagittal). All colors and labeling are as in Panel A).


Data Access

Data TypeDownload all or Query/FilterLicense

Images and Radiation Therapy Structures (DICOM, 17 GB)

   

(Download requires NBIA Data Retriever)

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Detailed Description

Image Statistics

Radiology Image Statistics

Modalities

CT, RTSTRUCT

Number of Patients

131

Number of Studies

131

Number of Series

262

Number of Images

23,490

Images Size (GB)17



Citations & Data Usage Policy

Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:

Data Citation

Thompson, R. F., Kanwar, A., Merz, B., Cohen, E., Fisher, H., Rana, S., Claunch, C., & Hung, A. (2023). Stress-Testing Pelvic Autosegmentation Algorithms Using Anatomical Edge Cases (Prostate Anatomical Edge Cases) (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/QSTF-ST65

Publication Citation

Kanwar, A., Merz, B., Claunch, C., Rana, S., Hung, A., & Thompson, R. F. (2023). Stress-testing pelvic autosegmentation algorithms using anatomical edge cases. In Physics and Imaging in Radiation Oncology (Vol. 25, p. 100413). Elsevier BV. https://doi.org/10.1016/j.phro.2023.100413

TCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7

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Version 1 (Current): Updated 2023/05/18

Data TypeDownload all or Query/FilterLicense

Images and Radiation Therapy Structures (DICOM, 17 GB)

   

(Download requires the NBIA Data Retriever)



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