An automatic deep-learning model-centric pipeline for hierarchical quantitative analysis of cerebrovascular structures is proposed to extract vascular (and also cortical) features hierarchically from 1) whole brain, 2) four common vascular regions including anterior cortical region (ACR), posterior cortical region (PCR), middle cortical region (MCR), and circle of willis region (CoWR), and 3) conventional 82 Brodmann areas after registration of each MRA volume to atlas defined in Montreal Neurological Institute (MNI) space. Utilizing this pipeline, we investigate hierarchically gender-stratified normative trajectories including cortical volume (CV) versus aging and arterial volume (AV) versus aging.
The dataset will be released soon.
Step 1: Data preprocessing
Step 2: CereVessSeg model and CereVessPro contrastive learning for cerebrovascular segmentation
Step 3: Hierarchically arterial and cortical volume feature extration