{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:32:00Z","timestamp":1772137920027,"version":"3.50.1"},"reference-count":53,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T00:00:00Z","timestamp":1744761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T00:00:00Z","timestamp":1744761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01CA237269"],"award-info":[{"award-number":["R01CA237269"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100007210","name":"Varian Medical Systems Inc.","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007210","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Radiotherapy treatment planning requires segmenting anatomical structures in various styles, influenced by guidelines, protocols, preferences, or dose planning needs. Deep learning-based auto-segmentation models, trained on anatomical definitions, may not match local clinicians\u2019 styles at new institutions. Adapting these models can be challenging without sufficient resources. We hypothesize that consistent differences between segmentation styles and anatomical definitions can be learned from initial patients and applied to pre-trained models for more precise segmentation. We propose a Prior-guided deep difference meta-learner (DDL) to learn and adapt these differences. We collected data from 440 patients for model development and 30 for testing. The dataset includes contours of the prostate clinical target volume (CTV), parotid, and rectum. We developed a deep learning framework that segments new images with a matching style using example styles as a prior, without model retraining. The pre-trained segmentation models were adapted to three different clinician styles for post-operative CTV for prostate, parotid gland, and rectum segmentation. We tested the model\u2019s ability to learn unseen styles and compared its performance with transfer learning, using varying amounts of prior patient style data (0\u201310 patients). Performance was quantitatively evaluated using dice similarity coefficient (DSC) and Hausdorff distance. With exposure to only three patients for the model, the average DSC (%) improved from 78.6, 71.9, 63.0, 69.6, 52.2 and 46.3\u201384.4, 77.8, 73.0, 77.8, 70.5, 68.1, for CTV\n                    <jats:sub>style1<\/jats:sub>\n                    , CTV\n                    <jats:sub>style2<\/jats:sub>\n                    , CTV\n                    <jats:sub>style3<\/jats:sub>\n                    , Parotid\n                    <jats:sub>superficial<\/jats:sub>\n                    , Rectum\n                    <jats:sub>superior<\/jats:sub>\n                    , and Rectum\n                    <jats:sub>posterior<\/jats:sub>\n                    , respectively. The proposed Prior-guided DDL is a fast and effortless network for adapting a structure to new styles. The improved segmentation accuracy may result in reduced contour editing time, providing a more efficient and streamlined clinical workflow.\n                  <\/jats:p>","DOI":"10.1088\/2632-2153\/adc970","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T19:01:09Z","timestamp":1743793269000},"page":"025016","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prior guided deep difference meta-learner for fast adaptation to stylized segmentation"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9590-0655","authenticated-orcid":true,"given":"Dan","family":"Nguyen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6258-4468","authenticated-orcid":true,"given":"Anjali","family":"Balagopal","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6697-7434","authenticated-orcid":true,"given":"Ti","family":"Bai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9043-1490","authenticated-orcid":true,"given":"Michael","family":"Dohopolski","sequence":"additional","affiliation":[]},{"given":"Mu-Han","family":"Lin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3083-6752","authenticated-orcid":true,"given":"Steve","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2025,4,16]]},"reference":[{"key":"mlstadc970bib1","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/S1361-8415(98)80022-4","article-title":"Image matching as a diffusion process: an analogy with Maxwell\u2019s demons","volume":"2","author":"Thirion","year":"1998","journal-title":"Med. 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