{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T13:59:12Z","timestamp":1791381552279,"version":"4.3.3"},"reference-count":28,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["R01 EB023909"],"award-info":[{"award-number":["R01 EB023909"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["5P30 CA023108\u201041"],"award-info":[{"award-number":["5P30 CA023108\u201041"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R44CA265654"],"award-info":[{"award-number":["R44CA265654"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cherenkov imaging enables real\u2010time visualization of megavoltage X\u2010ray or electron beam delivery to the patient during radiation therapy (RT). Bio\u2010morphological features, such as vasculature, seen in these images are patient\u2010specific signatures that can be used for verification of positioning and motion management that are essential to precise RT treatment. However, no concerted analysis of this biological feature\u2010based tracking has been utilized until now because of the slow speed and accuracy of conventional image processing for feature segmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to demonstrate the first deep learning framework for such an application, achieving video frame rate processing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>To address the challenge of limited annotation of bio\u2010morphological features in Cherenkov images, a transfer learning strategy was applied. A fundus photography dataset including 20,529 patch retina images with ground\u2010truth vessel annotation was used to pre\u2010train a ResNet based segmentation framework. Subsequently, a small Cherenkov dataset (1483 images from 212 treatment fractions of 19 breast cancer patients) with known annotated vasculature masks was used to fine\u2010tune the model for accurate segmentation prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The well\u2010trained model was tested on clinical Cherenkov dataset which was not used in fine\u2010tune steps. This deep learning framework achieved consistent and rapid segmentation of Cherenkov\u2010imaged bio\u2010morphological features on a test dataset containing 19 patients (179 images), including subcutaneous veins, scars, and pigmented skin. The average segmentation by the model achieved a Dice score of 0.85 and required less than 0.7 ms processing time per instance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The model demonstrated outstanding consistency against input image variances and speed compared to conventional manual segmentation methods, laying the foundation for online segmentation in real\u2010time monitoring in a prospective setting.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.18002","type":"journal-article","created":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T06:17:56Z","timestamp":1754720276000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Robust real\u2010time segmentation of bio\u2010morphological features in human cherenkov imaging during radiotherapy via deep learning"],"prefix":"10.1002","volume":"52","author":[{"given":"Shiru","family":"Wang","sequence":"first","affiliation":[{"name":"Thayer School of Engineering Dartmouth College  Hanover New Hampshire USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Chen","sequence":"additional","affiliation":[{"name":"Thayer School of Engineering Dartmouth College  Hanover New Hampshire USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lesley A.","family":"Jarvis","sequence":"additional","affiliation":[{"name":"Dartmouth Cancer Center Dartmouth Health  Lebanon New Hampshire USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yucheng","family":"Tang","sequence":"additional","affiliation":[{"name":"Nvidia Corp  Santa Clara California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David J.","family":"Gladstone","sequence":"additional","affiliation":[{"name":"Thayer School of Engineering Dartmouth College  Hanover New Hampshire USA"},{"name":"Dartmouth Cancer Center Dartmouth Health  Lebanon New Hampshire USA"},{"name":"Geisel School of Medicine Dartmouth College  Hanover New Hampshire USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kimberley S.","family":"Samkoe","sequence":"additional","affiliation":[{"name":"Thayer School of Engineering Dartmouth College  Hanover New Hampshire USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian W.","family":"Pogue","sequence":"additional","affiliation":[{"name":"Thayer School of Engineering Dartmouth College  Hanover New Hampshire USA"},{"name":"Department of Medical Physics University of Wisconsin\u2010Madison  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petr","family":"Bruza","sequence":"additional","affiliation":[{"name":"Thayer School of Engineering Dartmouth College  Hanover New Hampshire USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongxiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Thayer School of Engineering Dartmouth College  Hanover New Hampshire USA"},{"name":"Department of Radiation Oncology University of Missouri\u2010Columbia  Columbia Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,8,8]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.7150\/ijms.3635"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrclinonc.2012.194"},{"key":"e_1_2_8_4_1","article-title":"Radiation offers new cures, and ways to do harm","author":"Bogdanich W","journal-title":"The New York Times"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2020.11.013"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2014.01.046"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2815358"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/0360\u20103016(89)90125\u20109"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2022.06.009"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4906249"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.17071"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20211346"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/60\/1\/L1"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JBO.26.9.098003"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41568\u2010018\u20100016\u20105"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278\u2010019\u201000227\u2010x"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_8_18_1","unstructured":"HeK ZhangX RenS SunJ Deep Residual Learning for Image Recognition. 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