1101 Automated Catheter Segmentation and Tip Detection in Cerebral Angiography With Topology-Aware Geometric Deep Learning

INTRODUCTION: Visual perception of catheters and guidewires on x-ray fluoroscopy is essential for neurointervention. METHODS: Catheters and guidewires were manually annotated on 3831 fluoroscopy frames collected prospectively from 40 patients undergoing cerebral angiography. We proposed a topology-aware geometric deep learning method (TAG-DL) and compared it with the state-of-the-art deep learning segmentation models, UNet, nnUNet and TransUNet. All models were trained on frontal view sequences and tested on both frontal and lateral view sequences from unseen patients. Results were assessed with centerline Dice score and tip-distance error. RESULTS: The TAG-DL and nnUNet models outperformed TransUNet and UNet. The best performing model was nnUNet, achieving a mean centerline-Dice score of 0.98 ±0.01 and a median tip-distance error of 0.43 (IQR 0.88) mm. Incorporating digital subtraction masks, with or without contrast, significantly improved performance on unseen patients, further enabling exceptional performance on lateral view fluoroscopy despite not being trained on this view. CONCLUSIONS: These results are the first step towards AI augmentation for robotic neurointervention that could amplify the reach, productivity, and safety of a limited neurointerventional workforce.