ArticleRadiation oncology (London, England)2022
Pelvic U-Net: multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network.
Article in Radiation oncology (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 16 citations in OpenAlex.
- Practical deep learning solutions for prostate cancer segmentation and implementation in radiotherapy planning.Physical and engineering sciences in medicine · 2026Article
- Modality-AGnostic image Cascade (MAGIC) for multi-modality cardiac substructure segmentation.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026Article
- Evaluation of Model Performance and Clinical Usefulness in Automated Rectal Segmentation in CT for Prostate and Cervical Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis.Scientific data · 2025Article
- LUND-PROBE - LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset.Scientific data · 2025Article
- Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy.Journal of applied clinical medical physics · 2023Article
- Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment.Frontiers in public health · 2023Article
- DART-Net: A Novel Deep Learning Framework for Precise Radiotherapy Planning with Automated Multiorgan Segmentation and RTSTRUCT Generation.Journal of medical physicsArticle
- C2FAU-Net: A Deep Learning Approach with Multi-scale Strategy for Automated Delineation of Organs-at-risk in Cervical Cancer High-dose Rate Brachytherapy.Journal of medical physicsArticle
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10 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundDelineation of organs at risk (OAR) for anal cancer radiation therapy treatment planning is a manual and time-consuming process. Deep learning-based methods can accelerate and partially automate this task. The aim of this study was to develop and evaluate a deep learning model for automated and improved segmentations of OAR in the pelvic region.
methodsA 3D, deeply supervised U-Net architecture with shuffle attention, referred to as Pelvic U-Net, was trained on 143 computed tomography (CT) volumes, to segment OAR in the pelvic region, such as total bone marrow, rectum, bladder, and bowel structures. Model predictions were evaluated on an independent test dataset (n = 15) using the Dice similarity coefficient (DSC), the 95th percentile of the Hausdorff distance (HD
resultsWithout any manual corrections, mean DSC values of 97%, 87% and 94% were found for total bone marrow, rectum, and bladder. Mean DSC values for bowel cavity, all bowel, small bowel, and large bowel were 95%, 91%, 87% and 81%, respectively. Total bone marrow, bladder, and bowel cavity segmentations derived from our model were rated excellent (89%, 93%, 42%), good (9%, 5%, 42%), or acceptable (2%, 2%, 16%) on average. For almost all the evaluated DVH indices, no significant difference between model predictions and manual delineations was found. Delineation time per patient could be reduced from 40 to 12 min, including manual corrections of model predictions, and to 4 min without corrections.
conclusionsOur Pelvic U-Net led to credible and clinically applicable OAR segmentations and showed improved performance compared to previous studies. Even though manual adjustments were needed for some predicted structures, segmentation time could be reduced by 70% on average. This allows for an accelerated radiation therapy treatment planning workflow for anal cancer patients.
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