End-to-End Learning of a Conditional Random Field for Intra-retinal Layer Segmentation in Optical Coherence Tomography

Abstract

Intra-retinal layer segmentation of Optical Coherence Tomography images is critical in the assessment of ocular diseases. Existing Energy minimization based methods employ handcrafted cost terms to define their energy and are not robust to the presence of abnormalities. We propose a novel, Linearly Parameterized, Conditional Random Field (LP-CRF) model whose energy is learnt from a set of training images in an end-to-end manner. The proposed LP-CRF comprises two convolution filter banks to capture the appearance of each tissue region and boundary, the relative weights of the shape priors and an additional term based on the appearance similarity of the adjacent boundary points. All the energy terms are jointly learnt using the Structured Support Vector Machine. The proposed method segments all retinal boundaries in a single step. Our method was evaluated on 107 Normal and 220 AMD B-scan images and found to outperform three publicly available OCT segmentation software. The average unsigned boundary localization error is $1.52 \pm 0.29$ pixels for segmentation of 8 boundaries on the Normal dataset and $1.9 \pm 0.65$ pixels for 3 boundaries on the combined AMD and Normal dataset establishing the robustness of the proposed method.