{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:05:23Z","timestamp":1778083523373,"version":"3.51.4"},"reference-count":22,"publisher":"Optica Publishing Group","issue":"6","license":[{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/doi.org\/10.1364\/OA_License_v2#GOV"},{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/opg.optica.org\/policies\/opg-tdm-policy.json"}],"funder":[{"DOI":"10.13039\/100000053","name":"National Eye Institute","doi-asserted-by":"publisher","award":["EY000509"],"award-info":[{"award-number":["EY000509"]}],"id":[{"id":"10.13039\/100000053","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Opt. Express"],"published-print":{"date-parts":[[2022,6,1]]},"abstract":"<jats:p><jats:bold>Introduction \u2013<\/jats:bold> Retinal layer segmentation in\n\t\t\t\t\toptical coherence tomography (OCT) images is an important approach for\n\t\t\t\t\tdetecting and prognosing disease. Automating segmentation using robust\n\t\t\t\t\tmachine learning techniques lead to computationally efficient\n\t\t\t\t\tsolutions and significantly reduces the cost of labor-intensive\n\t\t\t\t\tlabeling, which is traditionally performed by trained graders at a\n\t\t\t\t\treading center, sometimes aided by semi-automated algorithms. Although\n\t\t\t\t\tseveral algorithms have been proposed since the revival of deep\n\t\t\t\t\tlearning, eyes with severe pathological conditions continue to\n\t\t\t\t\tchallenge fully automated segmentation approaches. There remains an\n\t\t\t\t\topportunity to leverage the underlying spatial correlations between\n\t\t\t\t\tthe retinal surfaces in the segmentation approach. <jats:bold>Methods\n\t\t\t\t\t-<\/jats:bold> Some of these proposed traditional methods can be expanded to\n\t\t\t\t\tutilize the three-dimensional spatial context governing the retinal\n\t\t\t\t\timage volumes by replacing the use of 2D filters with 3D filters.\n\t\t\t\t\tTowards this purpose, we propose a spatial-context, continuity and\n\t\t\t\t\tanatomical relationship preserving semantic segmentation algorithm,\n\t\t\t\t\twhich utilizes the 3D spatial context from the image volumes with the\n\t\t\t\t\tuse of 3D filters. We propose a 3D deep neural network capable of\n\t\t\t\t\tlearning the surface positions of the layers in the retinal volumes.\n\t\t\t\t\t<jats:bold>Results -<\/jats:bold> We utilize a dataset of OCT images from\n\t\t\t\t\tpatients with Age-related Macular Degeneration (AMD) to assess\n\t\t\t\t\tperformance of our model and provide both qualitative (including\n\t\t\t\t\tsegmentation maps and thickness maps) and quantitative (including\n\t\t\t\t\terror metric comparisons and volumetric comparisons) results, which\n\t\t\t\t\tdemonstrate that our proposed method performs favorably even for eyes\n\t\t\t\t\twith pathological changes caused by severe retinal diseases. The Mean\n\t\t\t\t\tAbsolute Error (MAE) and Root Mean Squared Error (RMSE) for patients\n\t\t\t\t\twith a wide range of AMD severity scores (0\u201311) were within\n\t\t\t\t\t0.84\u00b10.41 and 1.33\u00b10.73 pixels, respectively, which are\n\t\t\t\t\tsignificantly better than some of the other state-of-the-art\n\t\t\t\t\talgorithms. <jats:bold>Conclusion \u2013<\/jats:bold> The results demonstrate\n\t\t\t\t\tthe utility of extracting features from the entire OCT volume by\n\t\t\t\t\ttreating the volume as a correlated entity and show the benefit of\n\t\t\t\t\tutilizing 3D autoencoder based regression networks for smoothing the\n\t\t\t\t\tapproximated retinal layers by inducing shape based regularization\n\t\t\t\t\tconstraints.<\/jats:p>","DOI":"10.1364\/boe.450193","type":"journal-article","created":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T17:00:06Z","timestamp":1647622806000},"page":"3195","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Retinal layer segmentation in optical coherence tomography (OCT) using a 3D deep-convolutional regression network for patients with age-related macular degeneration"],"prefix":"10.1364","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0748-8371","authenticated-orcid":true,"given":"Souvick","family":"Mukherjee","sequence":"first","affiliation":[{"name":"Unit on Clinical Investigation of Retinal Disease"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tharindu","family":"De Silva","sequence":"additional","affiliation":[{"name":"Unit on Clinical Investigation of Retinal Disease"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peyton","family":"Grisso","sequence":"additional","affiliation":[{"name":"Unit on Clinical Investigation of Retinal Disease"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henry","family":"Wiley","sequence":"additional","affiliation":[{"name":"National Institutes of Health"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2253-1772","authenticated-orcid":true,"given":"D. 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Keenan","family":"Tiarnan","sequence":"additional","affiliation":[{"name":"National Institutes of Health"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alisa T","family":"Thavikulwat","sequence":"additional","affiliation":[{"name":"National Institutes of Health"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emily","family":"Chew","sequence":"additional","affiliation":[{"name":"National Institutes of Health"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Catherine","family":"Cukras","sequence":"additional","affiliation":[{"name":"Unit on Clinical Investigation of Retinal Disease"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"285","published-online":{"date-parts":[[2022,5,5]]},"reference":[{"key":"boe-13-6-3195-R1","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1001\/archopht.122.4.564","volume":"122","author":"Friedman","year":"2004","journal-title":"Arch.\n\t\t\t\t\tOphthalmol."},{"key":"boe-13-6-3195-R2","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.2165\/00003495-200868080-00002","volume":"68","author":"Chappelow","year":"2008","journal-title":"Drugs"},{"key":"boe-13-6-3195-R3","doi-asserted-by":"publisher","first-page":"2732","DOI":"10.1364\/BOE.8.002732","volume":"8","author":"Fang","year":"2017","journal-title":"Biomed. 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