Automated C-Arm Positioning via Conformal Landmark Localization
This work addresses the need for reduced radiation exposure and procedural delays in clinical workflows, though it is incremental as it builds on existing methods for uncertainty estimation and uses synthetic data.
The authors tackled the problem of manual C-arm positioning in fluoroscopy-guided interventions by developing an automated pipeline that predicts 3D displacement vectors to anatomical landmarks from X-ray images, achieving strong localization accuracy and well-calibrated prediction bounds on a synthetic dataset.
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