C-arm Guidance: A Self-supervised Approach To Automated Positioning During Stroke Thrombectomy
This work addresses the resource-intensive nature of thrombectomy procedures for stroke patients, though it appears incremental as it builds on existing methods with a novel pretext task.
The paper tackles the problem of automating C-arm positioning during stroke thrombectomy by introducing a self-supervised deep learning framework that classifies skeletal landmarks, and it reports outperforming existing methods in regression and classification tasks.
Thrombectomy is one of the most effective treatments for ischemic stroke, but it is resource and personnel-intensive. We propose employing deep learning to automate critical aspects of thrombectomy, thereby enhancing efficiency and safety. In this work, we introduce a self-supervised framework that classifies various skeletal landmarks using a regression-based pretext task. Our experiments demonstrate that our model outperforms existing methods in both regression and classification tasks. Notably, our results indicate that the positional pretext task significantly enhances downstream classification performance. Future work will focus on extending this framework toward fully autonomous C-arm control, aiming to optimize trajectories from the pelvis to the head during stroke thrombectomy procedures. All code used is available at https://github.com/AhmadArrabi/C_arm_guidance