Advancing Vascular Segmentation in Ferumoxytol-enhanced MRA: A Comparative Study of nnUNet and SegMamba

Motivation: Vascular segmentation in ferumoxytol-enhanced MR angiography (FE-MRA) is essential for diagnosing vascular diseases, but traditional methods often require time-consuming manual intervention. Deep learning models like nnUNet and SegMamba are promising to improve automation and diagnostic accuracy. Goal(s): This study compares the performance of nnUNet and SegMamba for automated vascular segmentation in FE-MRA. Approach: We evaluated both models with an FE-MRA dataset of 12 patients, using the Dice Similarity Coefficient to assess the segmentation accuracy. Results: nnUNet outperformed SegMamba, demonstrating a DSC of 0.853 and 0.828 when trained with data from 8 and 4 patients respectively. Impact: This study explores automated vascular segmentation in FE-MRA and demonstrates that nnUNet outperforms SegMamba, offering a more reliable and automated approach.