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Choroid segmentation in OCT images based on improved U-net
Author(s): Xuena Cheng; Xinjian Chen; Yuhui Ma; Weifang Zhu; Ying Fan; Fei Shi
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Paper Abstract

Change of the thickness and volume of the choroid, which can be observed and quantified from optical coherence tomography (OCT) images, is a feature of many retinal diseases, such as aged-related macular degeneration and myopic maculopathy. In this paper, we make purposeful improvements on the U-net for segmenting the choroid of either normal or pathological myopia retina, obtaining the Bruch’s membrane (BM) and the choroidal-scleral interface (CSI). There are two main improvements to the U-net framework: (1) Adding a refinement residual block (RRB) to the back of each encoder. This strengthens the recognition ability of each stage; (2) The channel attention block (CAB) is integrated with the U-net. This enables high-level semantic information to guide the underlying details and handle the intra-class inconsistency problem. We validated our improved network on a dataset which consists of 952 OCT Bscans obtained from 95 eyes from both normal subjects and patients suffering from pathological myopia. Comparing with manual segmentation, the mean choroid thickness difference is 8μm, and the mean Dice similarity coefficient is 85.0%.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094921 (15 March 2019); doi: 10.1117/12.2509407
Show Author Affiliations
Xuena Cheng, Soochow Univ. (China)
Xinjian Chen, Soochow Univ. (China)
Yuhui Ma, Soochow Univ. (China)
Weifang Zhu, Soochow Univ. (China)
Ying Fan, Shanghai General Hospital (China)
Fei Shi, Soochow Univ. (China)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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