Keywords: Vessels, Blood, brain blood vessel segmentation,Multi-Parametric,Multi-viewAccurate 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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords