2025/11/26 by Changki Mo, Mo, Chou, Yehyun Suh +5
Computer Science · Engineering · Medicine · #Augmented Reality Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Medical Imaging and Analysis #Surgical Simulation and Training
paper · pdf · doi:10.48550/arxiv.2511.21575
openalex publication_date 2025/11/26 · openalex created_date 2025/11/28 · openalex updated_date 2026/07/28
Automated landmark detection offers an efficient approach for medical professionals to understand patient anatomic structure and positioning using intra-operative imaging. While current detection methods for pelvic fluoroscopy demonstrate promising accuracy, most assume a fixed Antero-Posterior view of the pelvis. However, orientation often deviates from this standard view, either due to repositioning of the imaging unit or of the target structure itself. To address this limitation, we propose a novel framework that incorporates 2D/3D landmark registration into the training of a U-Net landmark prediction model. We analyze the performance difference by comparing landmark detection accuracy between the baseline U-Net, U-Net trained with Pose Estimation Loss, and U-Net fine-tuned with Pose Estimation Loss under realistic intra-operative conditions where patient pose is variable.