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Opt. Express"],"published-print":{"date-parts":[[2024,6,1]]},"abstract":"<jats:p>Accurate segmentation of retinal layers in optical coherence tomography\n\t\t\t\t\t(OCT) images is critical for assessing diseases that affect the optic\n\t\t\t\t\tnerve, but existing automated algorithms often fail when pathology\n\t\t\t\t\tcauses irregular layer topology, such as extreme thinning of the\n\t\t\t\t\tganglion cell-inner plexiform layer (GCIPL). Deep LOGISMOS, a hybrid\n\t\t\t\t\tapproach that combines the strengths of deep learning and 3D graph\n\t\t\t\t\tsearch to overcome their limitations, was developed to improve the\n\t\t\t\t\taccuracy, robustness and generalizability of retinal layer\n\t\t\t\t\tsegmentation. The method was trained on 124 OCT volumes from both eyes\n\t\t\t\t\tof 31 non-arteritic anterior ischemic optic neuropathy (NAION)\n\t\t\t\t\tpatients and tested on three cross-sectional datasets with available\n\t\t\t\t\treference tracings: Test-NAION (40 volumes from both eyes of 20 NAION\n\t\t\t\t\tsubjects), Test-G (29 volumes from 29 glaucoma subjects\/eyes), and\n\t\t\t\t\tTest-JHU (35 volumes from 21 multiple sclerosis and 14 control\n\t\t\t\t\tsubjects\/eyes) and one longitudinal dataset without reference\n\t\t\t\t\ttracings: Test-G-L (155 volumes from 15 glaucoma patients\/eyes). In\n\t\t\t\t\tthe three test datasets with reference tracings (Test-NAION, Test-G,\n\t\t\t\t\tand Test-JHU), Deep LOGISMOS achieved very high Dice similarity\n\t\t\t\t\tcoefficients (%) on GCIPL: 89.97\u00b13.59,\n\t\t\t\t\t90.63\u00b12.56, and 94.06\u00b11.76, respectively. In the same\n\t\t\t\t\tcontext, Deep LOGISMOS outperformed the Iowa reference algorithms by\n\t\t\t\t\timproving the Dice score by 17.5, 5.4, and 7.5, and also surpassed the\n\t\t\t\t\tdeep learning framework nnU-Net with improvements of 4.4, 3.7, and\n\t\t\t\t\t1.0. For the 15 severe glaucoma eyes with marked GCIPL thinning\n\t\t\t\t\t(Test-G-L), it demonstrated reliable regional GCIPL thickness\n\t\t\t\t\tmeasurement over five years. The proposed Deep LOGISMOS approach has\n\t\t\t\t\tpotential to enhance precise quantification of retinal structures,\n\t\t\t\t\taiding diagnosis and treatment management of optic nerve diseases.<\/jats:p>","DOI":"10.1364\/boe.516045","type":"journal-article","created":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T04:00:08Z","timestamp":1714708808000},"page":"3681","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve"],"prefix":"10.1364","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2725-6239","authenticated-orcid":true,"given":"Zhi","family":"Chen","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honghai","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward F.","family":"Linton","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brett A.","family":"Johnson","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun Jae","family":"Choi","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark J.","family":"Kupersmith","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milan","family":"Sonka","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mona K.","family":"Garvin","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Center for the Prevention and Treatment of Visual Loss"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Randy H.","family":"Kardon","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Center for the Prevention and Treatment of Visual Loss"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jui-Kai","family":"Wang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Center for the Prevention and Treatment of Visual Loss"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"key":"boe-15-6-3681-R1","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1016\/S1474-4422(10)70168-X","volume":"9","author":"Petzold","year":"2010","journal-title":"The Lancet\n\t\t\t\t\tNeurol."},{"key":"boe-15-6-3681-R2","doi-asserted-by":"publisher","first-page":"3588","DOI":"10.1167\/iovs.15-18736.","volume":"57","author":"Kupersmith","year":"2016","journal-title":"Investig. 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