Semi-Supervised Cerebrovascular Segmentation by Hierarchical Convolutional Neural Network

Due to the tortuosity and the complexity of cerebral vasculature and the similar intensity distribution with the background, it remains challenging to accurately segment cerebral vessels from magnetic resonance angiography (MRA). The previous rule-based methods have limitations when applied to accurate clinical diagnosis, such as the under-segmentation on complex vessels, the dependence on domain knowledge, and the lack of quantification estimation. In this paper, we proposed a semi-supervised cerebrovascular segmentation method with a hierarchical convolutional neural network (H-CNN) that transfers the exquisite model/feature design in rule-based methods to solve the mapping from MRA images to cerebral vessels. First, we generated the tube-level labels of cerebral vessels with centerlines and estimated radii. Second, we constructed and trained an H-CNN with the MRA images and corresponding tube-level labels. Third, the stopping criterion of the proposed H-CNN was determined by the comprehensive index (CI) that was defined based on partially annotated voxel-level ground truth. The comparison of our H-CNN with the vesselness, bi-Gaussian, optimally oriented flux, vessel enhancing diffusion, hybrid diffusion with continuous switch, Mimics software, convolutional neutral network_2D (CNN)_2D, and CNN_3D were conducted on six testing images. The mean sensitivity, accuracy, and the CI of our H-CNN are 94.69%, 97.85%, and 2.99%, respectively, outperforming the other methods. The curved planar reformation also visualized the performance of H-CNN for cerebrovascular segmentation. Given only the tube-level labels, the proposed H-CNN method accomplished the voxel-level vessel segmentation via the hierarchical update of CNN. The H-CNN can potentially to be applied for the accurate diagnosis of cerebrovascular diseases and other medical image segmentation with only partially correct labels.