Blood Vessel Segmentation Based on the 3D Residual U-Net

Abstract

In this paper, we propose blood vessel segmentation based on the 3D residual U-Net method. First, we integrate the residual block structure into the 3D U-Net. By exploring the influence of adding residual blocks at different positions in the 3D U-Net, we establish a novel and effective 3D residual U-Net. In addition, to address the challenges of pixel imbalance, vessel boundary segmentation, and small vessel segmentation, we develop a new weighted Dice loss function with a better effect than the weighted cross-entropy loss function. When training the model, we adopted a two-stage method from coarse-to-fine. In the fine stage, a local segmentation method of 3D sliding window is added. In the model testing phase, we used the 3D fixed-point method. Furthermore, we employ the 3D morphological closed operation to smooth the surfaces of vessels and volume analysis to remove noise blocks. To verify the accuracy and stability of our method, we compare our method with FCN, 3D DenseNet, and 3D U-Net. The experimental results indicate that our method has higher accuracy and better stability than the other studied methods and that the average Dice coefficients for hepatic veins and portal veins reach 71.7% and 76.5% in the coarse stage and 72.5% and 77.2% in the fine stage, respectively. In order to verify the robustness of the model, we conducted the same comparative experiment on the brain vessel datasets, and the average Dice coefficient reached 87.2%.