Semi-Supervised Automatic Layer and Fluid Region Segmentation of Retinal Optical Coherence Tomography Images Using Adversarial Learning

Abstract

Optical coherence tomography (OCT) is a primary imaging technique for ophthalmic diagnosis, which has the advantages of high-resolution and non-invasive. Diabetes is a chronic disease which might increase the risk of blindness. Hence, it is important to monitor the morphology of the retinal layer and fluid accumulation for Diabetic macular edema (DME) patients. In this paper, we proposed a new semi-supervised fully convolutional deep learning approach for segmenting retinal layers and fluid region in retinal OCT B-scans. The proposed semi -supervised approach leverages unlabeled data through an adversarial learning strategy. The segmentation framework includes a segment network and a discriminate network, both two networks are u-net like fully convolutional architecture. The objective function of the segment network is a joint loss function including multi-class cross entropy loss, adversarial loss and semi-supervise loss. Experiment result on the duke DME dataset demonstrate the effectiveness of the proposed segmentation framework.