SPNet: Sparse-mask Prompt-learning Network for Cerebrovascular Segmentation

Abstract

Cerebrovascular segmentation demands a comprehensive understanding of vascular topology from a global perspective on 3D Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) images. However, existing 3D segmentation methods typically rely on patch-wise processing to manage computational costs, where different segments of the same vessel branch may be handled independently in separate patches. This might restrict their ability to capture the full 3D context of the vessel, thus severely impacting segmentation performance. In this paper, we focus on achieving comprehensive 3D vessel segmentation in a single feedforward pass by leveraging Maximum Intensity Projection (MIP). By compressing the original 3D image, the vessels of the entire brain are superimposed and projected onto a 2D MIP image. We propose a Sparse-mask Prompt-learning Network (SPNet) to leverage MIP for precise cerebrovascular segmentation. Specifically, SPNet generates MIP images along three orthogonal directions and performs 2D segmentation on each of these MIP images independently. The segmented 2D vessels of MIPs are then back-projected into 3D space and fused into a sparse vessel mask. SPNet treats this sparse mask as a topological prompt that captures the overall vascular pathways, thereby enhancing feature learning on the original 3D TOF-MRA data, ultimately improving the final cerebrovascular segmentation performance. Experimental results on the ADAM and IXI datasets demonstrate superior performance of SPNet with least improvements of 1.73% and 0.75% on average Dices, 3.04mm and 1.19mm on Hausdorff distances, respectively, over state-of-the-art methods. Codes are available at: https://github.com/shanwq/SPNet.