{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T10:09:46Z","timestamp":1789034986229,"version":"build-2803163510"},"reference-count":32,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T00:00:00Z","timestamp":1656979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T00:00:00Z","timestamp":1656979200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"crossref","award":["No. 201706080096"],"award-info":[{"award-number":["No. 201706080096"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technol Cancer Res Treat"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:p>\n                    <jats:bold>Purpose:<\/jats:bold>\n                    To evaluate the accuracy of deep-learning-based auto-segmentation of the superior constrictor, middle constrictor, inferior constrictor, and larynx in comparison with a traditional multi-atlas-based method.\n                    <jats:bold>Methods and Materials:<\/jats:bold>\n                    One hundred and five computed tomography image datasets from 83 head and neck cancer patients were retrospectively collected and the superior constrictor, middle constrictor, inferior constrictor, and larynx were analyzed for deep-learning versus multi-atlas-based segmentation. Eighty-three computed tomography images (40 diagnostic computed tomography and 43 planning computed tomography) were used for training the convolutional neural network, and for atlas-based model training. The remaining 22 computed tomography datasets were used for validation of the atlas-based auto-segmentation versus deep-learning-based auto-segmentation contours, both of which were compared with the corresponding manual contours. Quantitative measures included Dice similarity coefficient, recall, precision, Hausdorff distance, 95th percentile of Hausdorff distance, and mean surface distance. Dosimetric differences between the auto-generated contours and manual contours were evaluated. Subjective evaluation was obtained from 3 clinical observers to blindly score the autosegmented structures based on the percentage of slices that require manual modification.\n                    <jats:bold>Results:<\/jats:bold>\n                    The deep-learning-based auto-segmentation versus atlas-based auto-segmentation results were compared for the superior constrictor, middle constrictor, inferior constrictor, and larynx. The mean Dice similarity coefficient values for the 4 structures were 0.67, 0.60, 0.65, and 0.84 for deep-learning-based auto-segmentation, whereas atlas-based auto-segmentation has Dice similarity coefficient results at 0.45, 0.36, 0.50, and 0.70, respectively. The mean 95th percentile of Hausdorff distance (cm) for the 4 structures were 0.41, 0.57, 0.59, and 0.54 for deep-learning-based auto-segmentation, but 0.78, 0.95, 0.96, and 1.23 for atlas-based auto-segmentation results, respectively. Similar mean dose differences were obtained from the 2 sets of autosegmented contours compared to manual contours. The dose\u2013volume discrepancies and the average modification rates were higher with the atlas-based auto-segmentation contours.\n                    <jats:bold>Conclusion:<\/jats:bold>\n                    Swallowing-related structures are more accurately generated with DL-based versus atlas-based segmentation when compared with manual contours.\n                  <\/jats:p>","DOI":"10.1177\/15330338221105724","type":"journal-article","created":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T21:49:55Z","timestamp":1657057795000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["Evaluating Automatic Segmentation for Swallowing-Related Organs for Head and Neck Cancer"],"prefix":"10.1177","volume":"21","author":[{"given":"Yimin","family":"Li","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Xiamen Radiotherapy Quality Control Center, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, The Third Clinical Medical College, Fujian Medical University, Xiamen, Fujian, China"},{"name":"University of California Davis Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8813-5879","authenticated-orcid":false,"given":"Shyam","family":"Rao","sequence":"additional","affiliation":[{"name":"University of California Davis Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Chen","sequence":"additional","affiliation":[{"name":"Xiangya Hospital, Central South University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soheila F.","family":"Azghadi","sequence":"additional","affiliation":[{"name":"University of California Davis Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ky Nam Bao","family":"Nguyen","sequence":"additional","affiliation":[{"name":"University of California Davis Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angel","family":"Moran","sequence":"additional","affiliation":[{"name":"University of California Davis Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brittni M","family":"Usera","sequence":"additional","affiliation":[{"name":"University of California Davis Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brandon A","family":"Dyer","sequence":"additional","affiliation":[{"name":"Legacy Health"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Shang","sequence":"additional","affiliation":[{"name":"University of California Davis Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5570-2462","authenticated-orcid":false,"given":"Quan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, City of Hope comprehensive Cancer Center, Duarte, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2620-1893","authenticated-orcid":false,"given":"Yi","family":"Rong","sequence":"additional","affiliation":[{"name":"University of California Davis Medical Center"},{"name":"Mayo Clinic Arizona"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2022,7,5]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2015.61.2994"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2005.04.010"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2004.05.050"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2007.07.009"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00066-014-0634-0"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1186\/1748-717X-7-32"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1118\/1.4871620"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.3109\/0284186X.2016.1173723"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2014.09.014"},{"key":"e_1_3_3_11_2","first-page":"434","article-title":"Atlas-based auto-segmentation of head and neck CT images","volume":"11","author":"Han X","year":"2008","unstructured":"Han X, Hoogeman MS, Levendag PC, et al. 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