GAPNet: Granularity Attention Network with Anatomy-Prior-Constraint for Carotid Artery Segmentation
This work addresses a domain-specific problem in medical imaging for diagnosing cardiovascular disease, likely incremental as it builds on prior segmentation methods.
The paper tackles the challenge of segmenting the carotid artery in MR black-blood vessel wall imaging due to complex neck anatomy and atherosclerosis, proposing GAPNet with a geometric prior to improve segmentation accuracy.
Atherosclerosis is a chronic, progressive disease that primarily affects the arterial walls. It is one of the major causes of cardiovascular disease. Magnetic Resonance (MR) black-blood vessel wall imaging (BB-VWI) offers crucial insights into vascular disease diagnosis by clearly visualizing vascular structures. However, the complex anatomy of the neck poses challenges in distinguishing the carotid artery (CA) from surrounding structures, especially with changes like atherosclerosis. In order to address these issues, we propose GAPNet, which is a consisting of a novel geometric prior deduced from.