用自监督学习自动定位脑卒中取栓术中的C臂，提升手术效率与安全。
C-arm Guidance: A Self-supervised Approach To Automated Positioning During Stroke Thrombectomy
- 通过回归预训练任务自监督学习骨骼关键点。
- 在分类与回归任务上均优于现有方法。
- 适合需要自动化影像引导的介入手术研究者。
脑卒中取栓术是治疗缺血性卒中的有效手段，但耗时耗力。本文提出利用深度学习自动化取栓过程中的关键环节，以提高效率与安全性。我们引入一种自监督框架，通过基于回归的预训练任务来分类多种骨骼关键点。实验表明，该模型在回归和分类任务中均优于现有方法。尤其值得注意的是，位置预训练任务显著提升了下游分类性能。未来工作将扩展此框架，实现C臂的全自动控制，优化从骨盆到头部的运动轨迹。所有代码已公开于 https://github.com/AhmadArrabi/C_arm_guidance。
原文摘要 · Abstract (English)
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
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