Dense Dilated Network with Probability Regularized Walk for Vessel Detection
This addresses vessel connectivity issues in retinal imaging for medical diagnosis, representing an incremental improvement over existing methods.
The paper tackled retinal vessel detection by proposing a dense dilated network for initial segmentation and a probability regularized walk algorithm to improve connectivity, achieving state-of-the-art results in accuracy, sensitivity, specificity, and AUC on three public datasets (DRIVE, STARE, CHASE_DB1).
The detection of retinal vessel is of great importance in the diagnosis and treatment of many ocular diseases. Many methods have been proposed for vessel detection. However, most of the algorithms neglect the connectivity of the vessels, which plays an important role in the diagnosis. In this paper, we propose a novel method for retinal vessel detection. The proposed method includes a dense dilated network to get an initial detection of the vessels and a probability regularized walk algorithm to address the fracture issue in the initial detection. The dense dilated network integrates newly proposed dense dilated feature extraction blocks into an encoder-decoder structure to extract and accumulate features at different scales. A multiscale Dice loss function is adopted to train the network. To improve the connectivity of the segmented vessels, we also introduce a probability regularized walk algorithm to connect the broken vessels. The proposed method has been applied on three public data sets: DRIVE, STARE and CHASE_DB1. The results show that the proposed method outperforms the state-of-the-art methods in accuracy, sensitivity, specificity and also are under receiver operating characteristic curve.