Automated Segmentation of Intracranial Arteries on 4D Flow MRI for Hemodynamic Quantification

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Abstract

Accurate vessel segmentation is essential for reliable hemodynamic quantification in 4D Flow MRI. Automated segmentation with deep learning offers a promising alternative to the time-consuming, operator-dependent manual segmentation, but its application is often hindered by the scarcity of labeled datasets. Moreover, the impact on downstream hemodynamic quantification remains to be investigated. We developed a transfer learning-based intracranial artery segmentation model using a 3D full-resolution nnU-Net, pretrained on 355 TOF-MRA scans and fine-tuned on 11 7T 4D Flow MRI scans. The model was compared with two published models (U-Net and DenseNet U-Net) against the manual reference, evaluating segmentation metrics on test sets of different resolutions and hemodynamic quantification. The proposed nnU-Net achieved the highest Dice score (>0.85), the lowest HD95 ([~]3 mm), and the highest ICCs in cross-sectional area (0.62-0.87, except PCAs) and mean blood flow (0.78- 0.98). For wall shear stress (WSS) quantification, nnU-Net segmentations achieved the closest agreement with the manual reference (mean = 1.57 {+/-} 0.63 Pa, ICC = 0.96; max = 2.16 {+/-} 1.05 Pa, ICC = 0.97) and minimal bias ([&le;] 1.7%), whereas U-Net and DenseNet U-Net showed systematic under-(-5%) and overestimation (+7%), respectively. However, several vessel segments, including the ACA for DenseNet U-Net and the BA for U-Net, showed statistically significant differences (ANOVA post-hoc correction P < 0.05) in the flow-related metrics when compared with the manual reference. These results demonstrate that transfer learning with nnU-Net provides a robust, fully automated solution for intracranial artery analysis, and that segmentation accuracy directly affects 4D Flow MRI-derived hemodynamic quantification.

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