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.
Highlights
Many effective methods for automatic retinal layers segmentation on normal retinal images have been reported[7,8,9,10,11]
The proposed framework was tested on a dataset of 25 SD-OCT images from 25 patients diagnosed with serous Pigment epithelium detachment (PED)
An automated framework is proposed for 3D serous PED segmentation in SD-OCT images, which effectively combines the multi-scale graph search, shape-constrained graph cut and mathematical morphology algorithm
Summary
Many effective methods for automatic retinal layers segmentation on normal retinal images have been reported[7,8,9,10,11]. Those segmentation methods tend to fail when the retina has serious deformation. Zheng et al used an interactive segmentation method to quantify the intraretinal and subretinal fluids in retinal SD-OCT volume scans from 37 patients with exudative AMD24. We propose an automated framework to segment serous PED by effectively combining the multi-scale graph search, shape-constrained graph cut and mathematical morphology algorithm. The contributions of this work are summarized as follows. (1) A novel automated framework is proposed for 3D serous PED segmentation in SD-OCT images, and the segmentation result demonstrates the efficiency and feasibility of the proposed framework. (2) The foreground and background seeds used in the shape-constrained graph cut algorithm are obtained automatically, which makes the proposed framework automatic. (3) An effective AdaBoost method is applied to remove false positive segmented regions in the initial segmentation results. (4) The mathematical morphology method is applied to refine the automatic segmentation result obtained from the shape-constrained graph cut method, in which the structure element is chosen adaptively
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