{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T14:07:19Z","timestamp":1790345239123,"version":"4.1.0"},"reference-count":22,"publisher":"Wiley","issue":"S2","license":[{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Medical Radiation Sci"],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>Contouring organs at risk (OARs) is a time\u2010intensive task that is a critical part of radiation therapy. Atlas\u2010based automatic segmentation has shown some success at reducing this time burden on practitioners; however, this method often requires significant manual editing to reach a clinically accurate standard. Deep learning (DL) auto\u2010segmentation has recently emerged as a promising solution. This study compares the accuracy of DL and atlas\u2010based auto\u2010segmentation in relation to clinical \u2018gold standard\u2019 reference contours.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Ninety CT datasets (30 head and neck, 30 thoracic, 30 pelvic) were automatically contoured using both atlas and DL segmentation techniques. Sixteen critical OARs were then quantitatively measured for accuracy using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). Qualitative analysis was performed to visually classify the accuracy of each structure into one of four explicitly defined categories. Additionally, the time to edit atlas and DL contours to a clinically acceptable level was recorded for a subset of 9 OARs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of the 16 OARs analysed, DL delivered statistically significant improvements over atlas segmentation in 13 OARs measured with DSC, 12 OARs measured with HD, and 12 OARs measured qualitatively. The mean editing time for the subset of DL contours was 50%, 23% and 61% faster (all <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.05) than that of atlas segmentation for the head and neck, thorax, and pelvis respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Deep learning segmentation comprehensively outperformed atlas\u2010based contouring for the majority of evaluated OARs. Improvements were observed in geometric accuracy and visual acceptability, while editing time was reduced leading to increased workflow efficiency.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmrs.618","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T08:45:03Z","timestamp":1663922703000},"page":"15-25","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Clinical evaluation of deep learning and atlas\u2010based auto\u2010segmentation for critical organs at risk in radiation therapy"],"prefix":"10.1002","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6953-9341","authenticated-orcid":false,"given":"Eddie","family":"Gibbons","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Mid North Coast Cancer Institute  Port Macquarie New South Wales 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