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Most deep learning systems adopt single\u2010fraction approaches that discard valuable temporal context from prior treatment\u00a0fractions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study develops AdaptSeg, a scalable framework leveraging cross\u2010fraction anatomical priors to substantially improve OAR segmentation without per\u2010patient\u00a0retraining.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We implemented a dual\u2010path neural architecture conditioning current fraction segmentation on paired image\u2010mask information from supporting fractions. AdaptSeg was instantiated with convolutional (3D UNet) and transformer\u2010based (SwinUNETR) backbones. Evaluation used 104 pancreatic cancer patients across 520 treatment fractions for four abdominal organs (colon, duodenum, small bowel, stomach), with patient\u2010level splitting: 72 training, 10 validation, 22 test patients. Performance metrics included Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95), and Average Symmetric Surface Distance (ASSD); paired comparisons used two\u2010sided Wilcoxon signed\u2010rank tests with Benjamini\u2013Hochberg correction, and 95% bootstrap confidence intervals for the\u00a0means.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Cross\u2010fraction priors improved segmentation performance for both tested backbones. The 3D UNet achieved 87.22% mean DSC versus 83.78% baseline, while SwinUNETR reached 85.19% versus 82.49% baseline. For highly deformable organs, improvements included up to 7.0 percentage point DSC gains (small bowel: 77.8% to 84.8%, ) and 62% boundary error reduction (colon HD95: 23.13 to 8.74 mm, ). Compared to nine state\u2010of\u2010the\u2010art methods, AdaptSeg achieved the best overall performance with substantial improvements in mean DSC (4.1%), HD95 (43%), and ASSD (39%) over the strongest baseline. All variants maintained computationally feasible inference under 1.6 s per case. Temporal prior selection showed a backbone\u2010dependent preference: the CNN favored sequential priors, and the transformer additionally benefited from randomized support sampling during training (sequential support is used at inference).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Cross\u2010fraction anatomical priors improved OAR segmentation for both tested backbone families, indicating that temporal context is an underutilized resource in fractionated radiotherapy. AdaptSeg provides a scalable, computationally feasible framework for accelerating MRgRT workflows without per\u2010patient adaptation, with sub\u20101.6 s inference compatible with the time constraints of online adaptive\u00a0treatment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70648","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T09:47:51Z","timestamp":1789120071000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross\u2010fraction prior learning for scalable organ\u2010at\u2010risk segmentation in abdominal MR\u2010guided radiotherapy"],"prefix":"10.1002","volume":"53","author":[{"given":"Chengyin","family":"Li","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Henry Ford Health Detroit Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doris","family":"Rusu","sequence":"additional","affiliation":[{"name":"Department of Computer Science Wayne State University Detroit Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafi Ibn","family":"Sultan","sequence":"additional","affiliation":[{"name":"Department of Computer Science Wayne State University Detroit Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hassan","family":"Bagher\u2010Ebadian","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Henry Ford Health Detroit Michigan USA"},{"name":"Department of Radiology Michigan State University East Lansing Michigan USA"},{"name":"College of Osteopathic Medicine Michigan State University East Lansing Michigan USA"},{"name":"Department of Physics Oakland University Rochester Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony","family":"Doemer","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Henry Ford Health Detroit Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongxiao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science Wayne State University Detroit Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parag J.","family":"Parikh","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Henry Ford Health Detroit Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kundan","family":"Thind","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Henry Ford Health Detroit Michigan USA"},{"name":"Department of Medicine Michigan State University East Lansing Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1148\/rycan.230011"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/jrr\/rrad090"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15174389"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.21037\/qims-21-168"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13040667"},{"key":"e_1_2_9_7_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010Net: Convolutional networks for biomedical image segmentation. 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