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. 2022 May 20;12:8508. doi: 10.1038/s41598-022-12486-w

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Application of lightweight DL model in segmentation of other ophthalmic images. (a–d) are the examples of segmentation of ILM + RNFL layer in OCT-B scan, (a) is the original B-scan from the testing dataset which has highest D, (b) is the corresponding manually segmented GT image, (c) is the segmented image overlapped on (a) with GT, and (d) is the segmented image from a testing dataset, which has lowest D. (e–h) are the examples of retinal vessel segmentation on a fundus photo from DRIVE dataset. (e) fundus photo, (f) manual segmentation (from grader-2 in dataset), (g) segmented with Unet_AB, and (h) with LWBNA_Unet model.