Automated Segmentation Of Retinal Serous Pigment Epithelium Detachment In OCT Images

Pigment epithelium detachment(PED) is an important clinical manifestation of multiple chorio-retinal diseases, such as age-related macular degeneration and central serous chorioretinopathy. PED can finally cause damage to central vision. Therefore, automatic segmentation for serous PED objects in spectral domain optical coherence tomography(SD-OCT) is of great clinical significance.In this paper, an automated framework is proposed to segment serous PEDs 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 algorithm 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 mathematical morphology method is applied to remove false positive 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. Linear regression analysis shows a strong correlation comparing the segmented PED volumes with the ground truth labeled by an ophthalmologist. The automatic segmentation result obtained from the proposed framework can provide clinicians with accurate quantitative information, including shape, size and position of the PED regions, which can assist diagnosis and treatment of associated diseases.