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

• Published in: IEEE transactions on biomedical engineering Vol. 65; no. 1; pp. 87 - 95
• Main Authors: Wu, Menglin, Chen, Qiang, He, XiaoJun, Li, Ping, Fan, Wen, Yuan, SongTao, Park, Hyunjin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; Central serous chorioretinopathy (CSC); Central Serous Chorioretinopathy - diagnostic imaging; Databases, Factual; Epithelium; Fluids; Fundus Oculi; Fuzzy Logic; Fuzzy sets; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Image segmentation; Imaging; Junctions; Level set; Medical imaging; neurosensory retinal detachment (NRD); Optical Coherence Tomography; Pigments; Retina; Retinal Detachment - diagnostic imaging; Smoothness; Spatial analysis; spectral domain optical coherence tomography (SD-OCT); Subretinal Fluid - diagnostic imaging; subretinal fluid segmentation; Tomography, Optical Coherence - methods
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2017.2695461

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.