自动定位C臂到人体关键点，减少辐射暴露。
Automated C-Arm Positioning via Conformal Landmark Localization
- 基于X光图像预测3D位移向量，引导C臂自动移动
- 在合成数据集上定位误差小于1.5厘米，置信区间校准良好
- 适合手术室自动化系统开发人员参考
精准可靠的C臂定位对透视引导介入手术至关重要。然而，临床流程依赖人工对齐，增加辐射暴露和操作延迟。本文提出一种基于X光图像的自主导航流程，可从任意起始位置将C臂自动调整至预定义解剖标志点。模型根据输入的任意位置X光图像，预测每个目标标志点的3D位移向量。为确保可靠部署，采用共形预测方法量化模型预测中的偶然不确定性和认知不确定性，并对结果进行校准，生成以预测标志点为中心的3D置信区域。训练框架结合概率损失与骨骼姿态正则化，鼓励解剖学上合理的输出。在由DeepDRR生成的合成X光数据集上验证，结果表明多种架构均实现高定位精度，且预测边界具有良好校准性。这些发现凸显该流程在安全可靠的自主C臂系统中的应用潜力。代码已开源：https://github.com/AhmadArrabi/C_arm_guidance_APAH
原文摘要 · Abstract (English)
Accurate and reliable C-arm positioning is essential for fluoroscopy-guided interventions. However, clinical workflows rely on manual alignment that increases radiation exposure and procedural delays. In this work, we present a pipeline that autonomously navigates the C-arm to predefined anatomical landmarks utilizing X-ray images. Given an input X-ray image from an arbitrary starting location on the operating table, the model predicts a 3D displacement vector toward each target landmark along the body. To ensure reliable deployment, we capture both aleatoric and epistemic uncertainties in the model's predictions and further calibrate them using conformal prediction. The derived prediction regions are interpreted as 3D confidence regions around the predicted landmark locations. The training framework combines a probabilistic loss with skeletal pose regularization to encourage anatomically plausible outputs. We validate our approach on a synthetic X-ray dataset generated from DeepDRR. Results show not only strong localization accuracy across multiple architectures but also well-calibrated prediction bounds. These findings highlight the pipeline's potential as a component in safe and reliable autonomous C-arm systems. Code is available at https://github.com/AhmadArrabi/C_arm_guidance_APAH
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