BLU-GAN: Bi-directional ConvLSTM U-Net with Generative Adversarial Training for Retinal Vessel Segmentation

Abstract

Retinal vascular morphometry is an important biomarker of eye-related cardiovascular diseases such as diabetes and hypertension. And retinal vessel segmentation is a fundamental step in fundus image analyses and diagnoses. In recent years, deep learning based networks have achieved superior performance in medical image segmentation. However, for fine vessels or terminal branches, most existing methods tend to miss or under-segment those structures, inducing isolated breakpoints. In this paper, we proposed Bi-Directional ConvLSTM U-Net with Generative Adversarial Training (BLU-GAN), a novel deep learning model based on U-Net that generates precise predictions of retinal vessels combined with generative adversarial training. Bi-directional ConvLSTM, which can better integrate features from different scales through a coarse-to-fine memory mechanism, is employed to non-linearly combine feature maps extracted from encoding path layers and the previous decoding up-convolutional layers and to replace the simple skip-connection used in the original U-Net. Moreover, we use densely connected convolutions in certain layers to strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. Through extensive experiments, BLU-GAN has shown leading performance among the state-of-the-art methods on the DRIVE, STARE, CHASE_DB1 datasets for retinal vessel segmentation.