{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T08:39:11Z","timestamp":1780043951439,"version":"3.53.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T00:00:00Z","timestamp":1636416000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T00:00:00Z","timestamp":1636416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Segmentation of important structures in temporal bone CT is the basis of image-guided otologic surgery. Manual segmentation of temporal bone CT is time- consuming and laborious. We assessed the feasibility and generalization ability of a proposed deep learning model for automated segmentation of critical structures in temporal bone CT scans.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Thirty-nine temporal bone CT volumes including 58 ears were divided into normal (n\u2009=\u200920) and abnormal groups (n\u2009=\u200938). Ossicular chain disruption (n\u2009=\u200910), facial nerve covering vestibular window (n\u2009=\u200910), and Mondini dysplasia (n\u2009=\u200918) were included in abnormal group. All facial nerves, auditory ossicles, and labyrinths of the normal group were manually segmented. For the abnormal group, aberrant structures were manually segmented. Temporal bone CT data were imported into the network in unmarked form. The Dice coefficient (DC) and average symmetric surface distance (ASSD) were used to evaluate the accuracy of automatic segmentation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In the normal group, the mean values of DC and ASSD were respectively 0.703, and 0.250\u00a0mm for the facial nerve; 0.910, and 0.081\u00a0mm for the labyrinth; and 0.855, and 0.107\u00a0mm for the ossicles. In the abnormal group, the mean values of DC and ASSD were respectively 0.506, and 1.049\u00a0mm for the malformed facial nerve; 0.775, and 0.298\u00a0mm for the deformed labyrinth; and 0.698, and 1.385\u00a0mm for the aberrant ossicles.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The proposed model has good generalization ability, which highlights the promise of this approach for otologist education, disease diagnosis, and preoperative planning for image-guided otology surgery.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-021-00698-x","type":"journal-article","created":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T12:03:08Z","timestamp":1636459388000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Fully automated segmentation in temporal bone CT with neural network: a preliminary assessment study"],"prefix":"10.1186","volume":"21","author":[{"given":"Jiang","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junchen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Furong","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yali","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Menglin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Ke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,9]]},"reference":[{"issue":"11","key":"698_CR1","doi-asserted-by":"publisher","first-page":"2797","DOI":"10.1007\/s00405-018-5101-6","volume":"275","author":"K Yamashita","year":"2018","unstructured":"Yamashita K, Hiwatashi A, Togao O, Kikuchi K, Matsumoto N, Momosaka D, Nakatake H, Sakai Y, Honda H. 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