Fully Automated Segmentation of Brain and Scalp Blood Vessels on Multi-Parametric Magnetic Resonance Imaging Using Multi-view Cascaded Network
Yang Yang, songxiong wu, Bingsheng Huang, Ping Zeng, Mingyu Wang, Zilong Huang · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2024
Accurate segmentation of blood vessels allow neurosurgical navigation and can help neurosurgeons accurate surgical and treatment plans. However, traditional blood vessel segmentation methods based on thresholds have limited performance. To solve problem, we proposed a cascaded DL network (MVPC-Net) that combines three refinements: multi-view learning, multi-parameter input, and a multi-view ensemble module-based strategy. The results of ablation experiments showed that, by adding all the refinements proposed, the performance of the baseline model improved from Dice similarity coefficient 0.865 to 0.922. Thus, our method can provide better segmentation of the brain, and scalp blood vessels and has potential for clinical application.