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Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images With Neurosensory Retinal Detachment Guided by Enface Fundus Imaging

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Title
Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images With Neurosensory Retinal Detachment Guided by Enface Fundus Imaging
Author(s)
Menglin Wu; Qiang Chen; XiaoJun He; Ping Li; Wen Fan; SongTao Yuan; Hyunjin Park
Subject
Central serous chorioretinopathy (CSC), ; neurosensory retinal detachment (NRD), ; spectral domain optical coherence tomography (SD-OCT), ; subretinal fluid segmentation
Publication Date
2018-01
Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, v.65, no.1, pp.87 - 95
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Abstract
Objective: Accurate segmentation of neurosensory retinal detachment (NRD) associated subretinal fluid in spectral domain optical coherence tomography (SD-OCT) is vital for the assessment of central serous chorioretinopathy (CSC). A novel two-stage segmentation algorithm was proposed, guided by Enface fundus imaging. Methods: In the first stage, Enface fundus image was segmented using thickness map prior to detecting the fluid-associated abnormalities with diffuse boundaries. In the second stage, the locations of the abnormalities were used to restrict the spatial extent of the fluid region, and a fuzzy level set method with a spatial smoothness constraint was applied to subretinal fluid segmentation in the SD-OCT scans. Results: Experimental results from 31 retinal SD-OCT volumes with CSC demonstrate that our method can achieve a true positive volume fraction (TPVF), false positive volume fraction (FPVF), and positive predicative value (PPV) of 94.3%, 0.97%, and 93.6%, respectively, for NRD regions. Our approach can also discriminate NRD-associated subretinal fluid from subretinal pigment epithelium fluid associated with pigment epithelial detachment with a TPVF, FPVF, and PPV of 93.8%, 0.40%, and 90.5%, respectively. Conclusion: We report a fully automatic method for the segmentation of subretinal fluid. Significance: Our method shows the potential to improve clinical therapy for CSC © 2017 IEEE.
URI
https://pr.ibs.re.kr/handle/8788114/4450
DOI
10.1109/TBME.2017.2695461
ISSN
0018-9294
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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