Monocular pose estimation of articulated open surgery tools -- in the wild
It solves the problem of estimating surgical tool poses without extensive manual annotation for medical professionals, though it appears incremental as it builds on existing domain adaptation and pose estimation methods.
This work tackled monocular 6D pose estimation of articulated surgical tools in open surgery, addressing challenges like specularity and occlusions, and demonstrated good performance on real data with potential for medical AR and robotics integration.
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.