🤖 AI Summary
This study addresses the high radiation exposure and low efficiency associated with manual C-arm positioning in fluoroscopy-guided surgery by proposing a fully automated method for 3D anatomical landmark localization. Methodologically, we design an end-to-end deep network that takes a single X-ray image as input and directly predicts a 3D displacement vector to the target anatomical landmark. To enhance anatomical plausibility, we introduce skeletal pose regularization and a probabilistic loss function; further, we integrate conformal prediction to quantify both aleatoric and epistemic uncertainty, yielding well-calibrated 3D confidence regions. Trained on DeepDRR-synthesized data, our approach achieves sub-centimeter accuracy (mean error <6 mm) across multiple network architectures, with empirically validated coverage of predicted intervals. To the best of our knowledge, this is the first work to jointly incorporate conformal prediction and anatomical constraints for autonomous C-arm navigation—significantly improving system safety, reliability, and clinical applicability.
📝 Abstract
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