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However, performing this segmentation on Computed Tomography (CT) images remains particularly challenging due to anatomical variability, limited soft\u2010tissue contrast, and the scarcity of annotated datasets. Compared to other radiotherapy domains, CT\u2010based gynecologic brachytherapy segmentation is notably underrepresented in benchmarking studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to improve the segmentation of HRCTV and OARs in gynecologic brachytherapy by introducing GynBTNet, a multi\u2010stage learning framework. Through large\u2010scale self\u2010supervised pretraining and progressive finetuning, the model is designed to enhance anatomical representation learning and adapt effectively to domain\u2010specific gynecologic structures, addressing the challenges of limited training data and complex anatomical variability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>GynBTNet employs a three\u2010stage training strategy: (1) self\u2010supervised pretraining on large\u2010scale CT datasets using sparse submanifold convolution to capture robust anatomical representations, (2) supervised finetuning on a multi\u2010organ segmentation dataset to refine feature extraction, and (3) task\u2010specific finetuning on the gynecologic brachytherapy dataset to optimize segmentation performance for clinical applications. In the third stage, 116 cases were used for training, while 29 cases were reserved for independent testing. The model was evaluated against state\u2010of\u2010the\u2010art methods using the Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95%), and Average Surface Distance (ASD). Overall statistical significance across models was assessed using the Friedman test. Post hoc pairwise comparisons were conducted using two\u2010tailed paired permutation tests, with multi\u2010comparison correction via the Benjamini\u2013Hochberg procedure to control the false discovery rate. Cohen's effect sizes were calculated to quantify the performance differences.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      GynBTNet demonstrated consistent superiority over nnU\u2010Net across all structures and achieved overall favorable performance compared to Swin\u2010UNETR. The most substantial improvement was observed in HRCTV segmentation, where GynBTNet achieved a DSC of 0.837\u00a0\u00b1\u00a00.068, significantly higher than nnU\u2010Net (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05) with a large effect size of +1.25, and superior to Swin\u2010UNETR with a moderate\u2010to\u2010large effect size of +0.57. Boundary precision for HRCTV also improved significantly, with effect sizes in HD95% (\u22120.81\u00a0vs. nnU\u2010Net, \u22120.52\u00a0vs. Swin\u2010UNETR) and ASD (\u22121.20\u00a0vs. nnU\u2010Net, \u22120.61\u00a0vs. Swin\u2010UNETR). For bladder segmentation, GynBTNet reached a DSC of 0.940\u00a0\u00b1\u00a00.052, significantly outperforming nnU\u2010Net (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05) with a large effect size of +1.28 and showing a small advantage over Swin\u2010UNETR (effect size +0.26). In rectum segmentation, GynBTNet achieved a DSC of 0.842\u00a0\u00b1\u00a00.070, significantly exceeding nnU\u2010Net (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05) with a large effect size of +1.17, and surpassing Swin\u2010UNETR with an effect size of +0.54. For the uterus, GynBTNet significantly improved boundary accuracy compared to both nnU\u2010Net and Swin\u2010UNETR (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05), with effect sizes in ASD of \u22120.99 and \u22120.64. Segmentation of the sigmoid colon remained challenging, as GynBTNet provided only marginal DSC gains over nnU\u2010Net with negligible effect sizes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed multi\u2010stage learning strategy effectively enhances segmentation accuracy for gynecologic brachytherapy, leveraging large\u2010scale self\u2010supervised pretraining and progressive finetuning. By improving HRCTV and OARs delineation, GynBTNet has the potential to enhance treatment planning precision, minimize radiation exposure to critical structures, and improve patient outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.18067","type":"journal-article","created":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T03:44:41Z","timestamp":1755315881000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Large\u2010scale convolutional neural network for clinical target and multi\u2010organ segmentation in gynecologic brachytherapy via multi\u2010stage learning"],"prefix":"10.1002","volume":"52","author":[{"given":"Mingzhe","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"},{"name":"Department of Computer Science and Mathematics Emory University  Atlanta Georgia 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