MF2ResU-Net: A Multi-Feature Fusion Deep Learning Architecture for Retinal Blood Vessel Segmentation

Abstract

Segmentation of blood vessels becomes an essential step in computer aided diagnosis system for the diseases in several departments of ophthalmology, neurosurgery, oncology, cardiology, and laryngology. Aiming at the problem of insufficient segmentation of small blood vessels by existing methods, a novel method based on multi-module fusion residual neural network model (MF2ResU-Net) was proposed. In the proposed networks, to obtain refined features of vessels, three cascade connected U-Net networks were employed as main networks. To deal with the problem of over-fitting, residual paths were used in main networks. In the blocks of U-Net in MF2ResU-Net, in order to remove the semantic difference in low-level and high-level, shortcut connections were used to combine the encoder layers and decoder layers in the blocks. Furthermore, atrous spatial pyramid pooling was embedded between the encoder and decoder to achieve multi-scale feature of blood vessels. During the training of the networks, to deal with the imbalance between background and foreground, a novel joint loss function was proposed based on the dice and cost- sensitive, which could greatly reduce the impact of unbalance in classes of samples. In experiment section, two retinal datasets, DRIVE and CHASE DB1, were used to test our method, and experiments showed that MF2ResU-Net was superior to existing methods on the criteria of sensitivity (Sen), specificity (Spe), accuracy (Acc), and area under curve (AUC), the values of which are 0.8013 and 0.8102, 0.9842 and 0.9809, 0.9700 and 0.9776, and 0.9797 and 0.9837 respectively for DRIVE and CHASE DB1. The results of experiments demonstrated the effectiveness and robustness of the model in the segmentation of complex curvature and small blood vessels.