ArticlePhysics and imaging in radiation oncology2023
Stress-testing pelvic autosegmentation algorithms using anatomical edge cases.
Article in Physics and imaging in radiation oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Can physics review catch prescription and PTV margin errors in fully automated radiotherapy plans? A hazard study.Journal of applied clinical medical physics · 2026Article
- Clinical edge-case stress testing of two Asia-developed AI auto-contouring systems using prostate radiotherapy planning CT images.Radiological physics and technology · 2026Article
- Adaptive Knowledge Distillation for Anatomical Segmentation in Pelvic CT Imaging of Prostate Cancer.Annals of biomedical engineering · 2026Article
- The impact of neck tilt on the accuracy of deep learning generated contours for CT images of the head and neck.Journal of applied clinical medical physics · 2025Article
- Pelvic dose accumulation accuracy in a CBCT based online adaptive radiotherapy system.Journal of applied clinical medical physics · 2025Article
- Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas.Radiation oncology (London, England) · 2024Article
- Geometric and dosimetric evaluation for breast and regional nodal auto-segmentation structures.Journal of applied clinical medical physics · 2024Article
- A clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy.Frontiers in oncology · 2023Article
- DART-Net: A Novel Deep Learning Framework for Precise Radiotherapy Planning with Automated Multiorgan Segmentation and RTSTRUCT Generation.Journal of medical physicsArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Commercial autosegmentation has entered clinical use, however real-world performance may suffer in certain cases. We aimed to assess the influence of anatomic variants on performance. We identified 112 prostate cancer patients with anatomic variations (edge cases). Pelvic anatomy was autosegmented using three commercial tools. To evaluate performance, Dice similarity coefficients, and mean surface and 95% Hausdorff distances were calculated versus clinician-delineated references. Deep learning autosegmentation outperformed atlas-based and model-based methods. However, edge case performance was lower versus the normal cohort (0.12 mean DSC reduction). Anatomic variation presents challenges to commercial autosegmentation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.