OCT Fluid Segmentation using Graph Shortest Path and Convolutional Neural Network^.

Diagnosis and monitoring of retina diseases related to pathologies such as accumulated fluid can be performed using optical coherence tomography (OCT). OCT acquires a series of 2D slices (Bscans). This work presents a fully-automated method based on graph shortest path algorithms and convolutional neural network (CNN) to segment and detect three types of fluid including sub-retinal fluid (SRF), intra-retinal fluid (IRF) and pigment epithelium detachment (PED) in OCT Bscans of subjects with age-related macular degeneration (AMD) and retinal vein occlusion (RVO) or diabetic retinopathy. The proposed method achieves an average dice coefficient of 76.44%, 92.25% and 82.14% in Cirrus, Spectralis and Topcon datasets, respectively. The effectiveness of the proposed methods was also demonstrated in segmenting fluid in OCT images from the 2017 Retouch challenge.