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Validation of automated artificial intelligence segmentation of optical coherence tomography images

Maloca, PM; Lee, AY; de Carvalho, ER; Okada, M; Fasler, K; Leung, I; Hörmann, B; ... Scholl, HPN; + view all (2019) Validation of automated artificial intelligence segmentation of optical coherence tomography images. PLoS One , 14 (8) , Article e0220063. 10.1371/journal.pone.0220063. Green open access

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Abstract

Purpose To benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera. Methods A convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results. Results The CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders’ compartmentalization was higher than the mean score for intra-grader group comparison. Conclusion The proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders.

Type: Article
Title: Validation of automated artificial intelligence segmentation of optical coherence tomography images
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0220063
Publisher version: https://doi.org/10.1371/journal.pone.0220063
Language: English
Additional information: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. https://creativecommons.org/publicdomain/zero/1.0/
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
URI: https://discovery.ucl.ac.uk/id/eprint/10080377
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