title: An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images.
abstract: Pigment epithelium detachment (PED) is an important clinical manifestation of multiple chorioretinal diseases, which can cause loss of central vision. In this paper, an automated framework is proposed to segment serous PED in SD-OCT images. The proposed framework consists of four main steps: first, a multi-scale graph search method is applied to segment abnormal retinal layers; second, an effective AdaBoost method is applied to refine the initial segmented regions based on 62 extracted features; third, a shape-constrained graph cut method is applied to segment serous PED, in which the foreground and background seeds are obtained automatically; finally, an adaptive structure elements based morphology method is applied to remove false positive segmented regions. The proposed framework was tested on 25 SD-OCT volumes from 25 patients diagnosed with serous PED. The average true positive volume fraction (TPVF), false positive volume fraction (FPVF), dice similarity coefficient (DSC) and positive predictive value (PPV) are 90.08%, 0.22%, 91.20% and 92.62%, respectively. The proposed framework can provide clinicians with accurate quantitative information, including shape, size and position of the PED region, which can assist clinical diagnosis and treatment.
authors.affiliation: School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu, 215006, China.
authors.forename: Zhuli
authors.initials: Z
authors.lastname: Sun
authors.affiliation: Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, Guangdong, 515041, China.
authors.forename: Haoyu
authors.initials: H
authors.lastname: Chen
authors.affiliation: School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu, 215006, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Shi
authors.affiliation: School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu, 215006, China.
authors.forename: Lirong
authors.initials: L
authors.lastname: Wang
authors.affiliation: School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu, 215006, China.
authors.forename: Weifang
authors.initials: W
authors.lastname: Zhu
authors.affiliation: School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu, 215006, China.
authors.forename: Dehui
authors.initials: D
authors.lastname: Xiang
authors.affiliation: College of Physics, Optoelectronics and Energy, Soochow University, Suzhou, Jiangsu, 215006, China.
authors.forename: Chenglin
authors.initials: C
authors.lastname: Yan
authors.affiliation: College of Physics, Optoelectronics and Energy, Soochow University, Suzhou, Jiangsu, 215006, China.
authors.forename: Liang
authors.initials: L
authors.lastname: Li
authors.affiliation: School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu, 215006, China.
authors.forename: Xinjian
authors.initials: X
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data*; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000706:statistics & numerical data*; D012163:Retinal Detachment / Q000000981:diagnostic imaging* / Q000473:pathology; D055213:Retinal Pigment Epithelium / Q000473:pathology*; D041623:Tomography, Optical Coherence / Q000379:methods / Q000706:statistics & numerical data*