A Novel Approach for Repairing Unsegmented Liver Vascular Images based on Centerline
Lisha Zhao, Zixuan Wang, Kun Dai, Quan Qi · 2023
In liver vascular intervention surgery, the absence or narrowing vascular images may impact the understanding of patients’ vascular structure and morphology. Repairing and analyzing vascular images is a crucial task in computer-aided diagnosis and surgery for minimally invasive vascular diseases. Previous research has addressed the problem of repairing small areas with regular shapes or minor curvature changes in the holes, often leading to modifications to the existing points and encounter noise and geometric loss. Unlike existing point cloud completion algorithms, this paper aims to address the issue of missing vessels in liver vascular images that have not been segmented. It employs axis-aligned bounding boxes to extract missing vascular images. Based on the geometric characteristics of the liver vascular centerline, two methods of cross-section and spherical wave propagation are proposed for repair process. These methods take localized missing vascular images as input and preserve the complete topological structure of the vessels. The repair results are evaluated using the Chamfer distance and Dice similarity coefficient. Experimental results demonstrate that the proposed approach effectively repairs large-scale, non-closed missing vascular images in liver vascular images and generates complete vessel geometric models with accurate liver vascular structure and morphology. The validity of the vessel models is confirmed through hemodynamic analysis, providing robust support for medical image analysis and diagnosis.