{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T18:03:53Z","timestamp":1789668233822,"version":"4.0.1"},"reference-count":34,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2021,5,16]],"date-time":"2021-05-16T00:00:00Z","timestamp":1621123200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100008637","name":"Center for Strategic Scientific Initiatives, National Cancer Institute","doi-asserted-by":"publisher","award":["R01CA237277"],"award-info":[{"award-number":["R01CA237277"]}],"id":[{"id":"10.13039\/100008637","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R01HL096613"],"award-info":[{"award-number":["R01HL096613"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:sec><jats:title>Objectives<\/jats:title><jats:p>To develop and validate a deep learning algorithm to automatically detect and segment an orbital abscess depicted on computed tomography (CT).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We retrospectively collected orbital CT scans acquired on 67 pediatric subjects with a confirmed orbital abscess in the setting of infectious orbital cellulitis. A context\u2010aware convolutional neural network (CA\u2010CNN) was developed and trained to automatically segment orbital abscess. To reduce the requirement for a large dataset, transfer learning was used by leveraging a pre\u2010trained model for CT\u2010based lung segmentation. An ophthalmologist manually delineated orbital abscesses depicted on the CT images. The classical U\u2010Net and the CA\u2010CNN models with and without transfer learning were trained and tested on the collected dataset using the 10\u2010fold cross\u2010validation method. Dice coefficient, Jaccard index, and Hausdorff distance were used as performance metrics to assess the agreement between the computerized and manual segmentations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The context\u2010aware U\u2010Net with transfer learning achieved an average Dice coefficient and Jaccard index of 0.78\u00a0\u00b1\u00a00.12 and 0.65\u00a0\u00b1\u00a00.13, which were consistently higher than the classical U\u2010Net or the context\u2010aware U\u2010Net without transfer learning (<jats:italic>P<\/jats:italic>\u00a0&lt;\u00a00.01). The average differences of the abscess between the computerized results and the experts in terms of volume and Hausdorff distance were 0.10\u00a0\u00b1\u00a00.11\u00a0mL and 1.94\u00a0\u00b1\u00a01.21\u00a0mm, respectively. The context\u2010aware U\u2010Net detected all orbital abscess without false positives.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The deep learning solution demonstrated promising performance in detecting and segmenting orbital abscesses on CT images in strong agreement with a human observer.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.14907","type":"journal-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T21:17:19Z","timestamp":1619558239000},"page":"3721-3729","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Automated delineation of orbital abscess depicted on CT scan using deep learning"],"prefix":"10.1002","volume":"48","author":[{"given":"Roxana","family":"Fu","sequence":"first","affiliation":[{"name":"Department of Ophthalmology University of Pittsburgh Pittsburgh PA 15213 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph K.","family":"Leader","sequence":"additional","affiliation":[{"name":"Departments of Radiology and Bioengineering University of Pittsburgh Pittsburgh PA 15213 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tejus","family":"Pradeep","sequence":"additional","affiliation":[{"name":"School of Medicine Johns Hopkins University Baltimore MD USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junli","family":"Shi","sequence":"additional","affiliation":[{"name":"Departments of Radiology and Bioengineering University of Pittsburgh Pittsburgh PA 15213 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Meng","sequence":"additional","affiliation":[{"name":"Departments of Radiology and Bioengineering University of Pittsburgh Pittsburgh PA 15213 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanchun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Departments of Radiology and Bioengineering University of Pittsburgh Pittsburgh PA 15213 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiantao","family":"Pu","sequence":"additional","affiliation":[{"name":"Departments of Radiology and Bioengineering University of Pittsburgh Pittsburgh PA 15213 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,5,16]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1542\/peds.2013-1071"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoto.2014.2626"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3955(16)34065-2"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1136\/pgmj.70.820.108"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/0091-6749(92)90184-4"},{"key":"e_1_2_9_7_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1017\/S0022215100600269","article-title":"The acute orbit. 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