Analysis of major segmentation models for intracranial artery time-of-flight magnetic resonance angiography images

Intracranial arterial stenosis (ICAS) is a leading cause of cerebrovascular accidents, and accurate morphological assessment of intracranial arteries is critical for diagnosis and treatment planning. Complex vascular structures, imaging noise, and variability in time-of-flight magnetic resonance angiography (TOF-MRA) images are challenging issues for the manual delineation that motivates the use of deep learning (DL) for automatic segmentation of the intracranial arteries. DL based automatic segmentation offers a promising solution by providing consistent and noise-reduced vessel delineation. However, selecting an optimal segmentation architecture remains challenging due to the diversity of network designs and encoder backbones. Therefore, this study presents a systematic benchmarking of four widely used DL segmentation architectures, UNet, LinkNet, Feature Pyramid Networks (FPN), Pyramid Scene Parsing Network (PSPNet), and DeepLabV3+, each combined with nine backbone networks, yielding 45 model variants, including previously unexplored configurations for intracranial artery segmentation in TOF-MRA. Models were trained and cross-validated on four datasets: in-house, CereVessMRA, IXI and ADAM, and evaluated on held-out independent test set. Performance metrics included Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and a Stability Score, combining the coefficient of variation of IoU and DSC to quantify segmentation consistency and reproducibility. Experimental results demonstrated highest DSC score was achieved with UNet–SE-ResNeXt50, LinkNet-SE-ResNeXt50, FPN-DenseNet169, FPN-SENet154. The most stable configurations were LinkNet–EfficientNetB6, LinkNet–SENet154, UNet–DenseNet169, and UNet–EfficientNetB6. Conversely, DeepLabV3+ and PSPNet variants consistently underperformed. These findings provide actionable guidance for selecting backbone–segmentation pairs and highlight trade-offs between accuracy, robustness, and reproducibility for complex intracranial artery TOF-MRA segmentation tasks.