{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T14:57:52Z","timestamp":1773500272440,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T00:00:00Z","timestamp":1656201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Computed tomography (CT) imaging of the orbit with measurement of extraocular muscle size can be useful for diagnosing and monitoring conditions that affect extraocular muscles. However, the manual measurement of extraocular muscle size can be time-consuming and tedious. The purpose of this study is to evaluate the effectiveness of deep learning algorithms in segmenting extraocular muscles and measuring muscle sizes from CT images. Consecutive CT scans of orbits from 210 patients between 1 January 2010 and 31 December 2019 were used. Extraocular muscles were manually annotated in the studies, which were then used to train the deep learning algorithms. The proposed U-net algorithm can segment extraocular muscles on coronal slices of 32 test samples with an average dice score of 0.92. The thickness and area measurements from predicted segmentations had a mean absolute error (MAE) of 0.35 mm and 3.87 mm2, respectively, with a corresponding mean absolute percentage error (MAPE) of 7 and 9%, respectively. On qualitative analysis of 32 test samples, 30 predicted segmentations from the U-net algorithm were accepted while 2 were rejected. Based on the results from quantitative and qualitative evaluation, this study demonstrates that CNN-based deep learning algorithms are effective at segmenting extraocular muscles and measuring muscles sizes.<\/jats:p>","DOI":"10.3390\/diagnostics12071553","type":"journal-article","created":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T09:00:13Z","timestamp":1656234013000},"page":"1553","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Semantic Segmentation of Extraocular Muscles on Computed Tomography Images Using Convolutional Neural Networks"],"prefix":"10.3390","volume":"12","author":[{"given":"Ramkumar Rajabathar Babu Jai","family":"Shanker","sequence":"first","affiliation":[{"name":"Department of Radiology, University of Chicago, Chicago, IL 60615, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael H.","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Chicago, Chicago, IL 60615, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3301-4364","authenticated-orcid":false,"given":"Daniel T.","family":"Ginat","sequence":"additional","affiliation":[{"name":"Department of Radiology, Section of Neuroradiology, University of Chicago, Chicago, IL 60615, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.2214\/ajr.179.4.1791005","article-title":"Using morphologic parameters of extraocular muscles for diagnosis and follow-up of Graves\u2019 ophthalmopathy: Diameters, areas, or volumes?","volume":"179","author":"Toth","year":"2002","journal-title":"AJR Am. 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