2024/07/16 by Robert Spektor, Spektor, Robert, Tom Friedman +7 · 1 citation
Engineering · Medicine · #Anatomy and Medical Technology #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging in Medicine #FOS: Computer and information sciences #Machine Learning (cs.LG) #Robotics (cs.RO) #Surgical Simulation and Training
paper · pdf · doi:10.48550/arxiv.2407.12138
openalex publication_date 2024/07/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
This work presents a framework for monocular 6D pose estimation of surgical instruments in open surgery, addressing challenges such as object articulations, specularity, occlusions, and synthetic-to-real domain adaptation. The proposed approach consists of three main components: (1) synthetic data generation pipeline that incorporates 3D scanning of surgical tools with articulation rigging and physically-based rendering; (2) a tailored pose estimation framework combining tool detection with pose and articulation estimation; and (3) a training strategy on synthetic and real unannotated video data, employing domain adaptation with automatically generated pseudo-labels. Evaluations conducted on real data of open surgery demonstrate the good performance and real-world applicability of the proposed framework, highlighting its potential for integration into medical augmented reality and robotic systems. The approach eliminates the need for extensive manual annotation of real surgical data.