{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T03:01:57Z","timestamp":1773025317553,"version":"3.50.1"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,2,16]],"date-time":"2025-02-16T00:00:00Z","timestamp":1739664000000},"content-version":"vor","delay-in-days":46,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 113-2321-B-A49-015"],"award-info":[{"award-number":["NSTC 113-2321-B-A49-015"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Biomedical Imaging"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>\n                    <jats:bold>Purpose:<\/jats:bold>\n                    Accurate segmentation of the cisternal segment of the trigeminal nerve plays a critical role in identifying and treating different trigeminal nerve\u2013related disorders, including trigeminal neuralgia (TN). However, the current manual segmentation process is prone to interobserver variability and consumes a significant amount of time. To overcome this challenge, we propose a deep learning\u2013based approach, U\u2010Net, that automatically segments the cisternal segment of the trigeminal nerve.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    To evaluate the efficacy of our proposed approach, the U\u2010Net model was trained and validated on healthy control images and tested in on a separate dataset of TN patients. The methods such as Dice, Jaccard, positive predictive value (PPV), sensitivity (SEN), center\u2010of\u2010mass distance (CMD), and Hausdorff distance were used to assess segmentation performance.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    Our approach achieved high accuracy in segmenting the cisternal segment of the trigeminal nerve, demonstrating robust performance and comparable results to those obtained by participating radiologists.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusion:<\/jats:bold>\n                    The proposed deep learning\u2013based approach, U\u2010Net, shows promise in improving the accuracy and efficiency of segmenting the cisternal segment of the trigeminal nerve. To the best of our knowledge, this is the first fully automated segmentation method for the trigeminal nerve in anatomic MRI, and it has the potential to aid in the diagnosis and treatment of various trigeminal nerve\u2013related disorders, such as TN.\n                  <\/jats:p>","DOI":"10.1155\/ijbi\/6694599","type":"journal-article","created":{"date-parts":[[2025,2,16]],"date-time":"2025-02-16T07:04:51Z","timestamp":1739689491000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Segmentation of the Cisternal Segment of Trigeminal Nerve on MRI Using Deep Learning"],"prefix":"10.1155","volume":"2025","author":[{"given":"Li-Ming","family":"Hsu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng-Wei","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Li","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jen-Tsung","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5594-5824","authenticated-orcid":false,"given":"Ching-Po","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4906-0365","authenticated-orcid":false,"given":"Yuan-Hsiung","family":"Tsai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,2,16]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awu349"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1177\/0333102413485658"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clineuro.2014.11.005"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00701-015-2459-8"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.wneu.2020.06.147"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1093\/neuros\/nyx636"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0316-z"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2017.12.026"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.801008"},{"key":"e_1_2_10_10_2","volume-title":"Keras Documentation","author":"Chollet F.","year":"2015"},{"key":"e_1_2_10_11_2","unstructured":"Mart\u00ednA. 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