Fully automated detection and quantification of multiple retinal lesions in OCT volumes based on deep learning and improved DRLSE

Automated and quantitative analysis of the retinal lesions region is very needed in clinical practice. In this paper, we have proposed a method which effectively combines deep learning and improved distance regularized level set evolution (DRLSE) for automatically detecting and segmenting multiple retinal lesions in OCT volumes. The proposed method can segment five different retinal lesions: pigment epithelium detachment (PED), sub-retinal fluid (SRF), drusen, choroidal neovascularization (CNV), macular holes (MH). We tested 500 B-scans from 15 3D OCT volumes. The experimental results have validated the effectiveness and efficiency of the proposed method. The quantitative indices of average precision (AP), area under the curve (AUC) at intersection-over-union (IoU) that is equal to 0.50 : 0.05 : 0.95 and dice similarity coefficient (DICE) in average of 93.2%, 90.6% and 90.3% can be achieved, respectively.