MRU-NET: A U-Shaped Network for Retinal Vessel Segmentation

Abstract

Fundus blood vessel image segmentation plays an important role in the diagnosis and treatment of diseases and is the basis of computer-aided diagnosis. Feature information from the retinal blood vessel image is relatively complicated, and the existing algorithms are sometimes difficult to perform effective segmentation with. Aiming at the problems of low accuracy and low sensitivity of the existing segmentation methods, an improved U-shaped neural network (MRU-NET) segmentation method for retinal vessels was proposed. Firstly, the image enhancement algorithm and random segmentation method are used to solve the problems of low contrast and insufficient image data of the original image. Moreover, smaller image blocks after random segmentation are helpful to reduce the complexity of the U-shaped neural network model; secondly, the residual learning is introduced into the encoder and decoder to improve the efficiency of feature use and to reduce information loss, and a feature fusion module is introduced between the encoder and decoder to extract image features with different granularities; and finally, a feature balancing module is added to the skip connections to resolve the semantic gap between low-dimensional features in the encoder and high-dimensional features in decoder. Experimental results show that our method has better accuracy and sensitivity on the DRIVE and STARE datasets (accuracy (ACC) = 0.9611, sensitivity (SE) = 0.8613; STARE: ACC = 0.9662, SE = 0.7887) than some of the state-of-the-art methods.

Generative Adversarial Network Combined with SE-ResNet and Dilated Inception Block for Segmenting Retinal Vessels

“…However, as the network level deepens, the problem of the gradient disappearance easily occurs, and the emergence of residual learning and dense connections can alleviate this problem. For example, Ding et al [ 18 ] have introduced the residual learning, feature fusion, and balance modules in the proposed MRU-Net, and achieved high accuracy on the retinal data set. Alom et al [ 19 ] have proposed a cyclic residual model based on U-Net that can extract the effective segmentation features and segmentation performance of the network, and it achieved good segmentation results.…”

Machine Learning for Biomedical Application

“…Another important application of ML to fundus blood vessel image segmentation is presented in [5]. Such analysis is important in the diagnosis and treatment of several diseases, such as hypertension, coronary heart disease, and diabetes.…”