Topology‐aware anatomical segmentation of the Circle of Willis: HUNet unveils the vascular network

This research investigates the Circle of Willis, a critical vascular structure vital for cerebral blood supply. A modified novel dual‐pathway multi‐scale hierarchical upsampling network (HUNet) is presented, tailored explicitly for accurate segmentation of Circle of Willis anatomical components from medical imaging data. Evaluating both the multi‐label magnetic resonance angiography region of interest and the multi‐label magnetic resonance angiography whole brain‐case datasets, HUNet consistently outperforms the convolutional U‐net model, demonstrating superior capabilities and achieving higher accuracy across various classes. Additionally, the HUNet model achieves an exceptional dice similarity coefficient of 98.61 and 97.95, along with intersection over union scores of 73.32 and 85.76 in both the multi‐label magnetic resonance angiography region of interest and the multi‐label magnetic resonance angiography whole brain‐case datasets, respectively. These metrics highlight HUNet's exceptional performance in achieving precise and accurate segmentation of anatomical structures within the Circle of Willis, underscoring its robustness in medical image segmentation tasks. Visual representations substantiate HUNet's efficacy in delineating Circle of Willis structures, offering comprehensive insights into its superior performance. This research introduces a modified dual‐pathway multi‐scale hierarchical upsampling network (HUNet) designed for accurate segmentation of the Circle of Willis from medical imaging data. HUNet consistently outperforms the conventional convolutional U‐net model across various datasets, achieving higher accuracy and demonstrating superior capabilities. Exceptional performance metrics include a dice similarity coefficient of 98.61 and 97.95, along with intersection over union scores of 73.32 and 85.76 for different datasets, and visual representations confirm HUNet's efficacy in precisely delineating Circle of Willis structures, showcasing its robustness in medical image segmentation tasks.