Semantic RGB--Depth Based Surgical Skill Assessment in Microscopic Stereo Videos

Jecia Z. Y. Mao, Sue M. Cho, Francis X. Creighton, Deepa Galaiya, Russell H. Taylor +1

Objective assessment of microsurgical technical skill is essential for competency-based training and quality assurance, yet existing video-based approaches predominantly rely on RGB images and therefore overlook the 3D spatial relationships that characterize instrument-anatomy interactions. Although stereo operating microscopes provide complementary depth information, conventional stereo matching algorithms can produce sparse and unreliable depth estimates under high-magnification imaging conditions, limiting their use for automated skill assessment. This work presents a semantic RGB-Depth framework for surgical skill assessment from microscopic stereo videos. A regression-based depth fusion method combines sparse metric stereo depth with dense monocular depth estimates to generate a dense geometric representation of the surgical scene. This representation is integrated with semantically decomposed RGB streams corresponding to individual surgical instruments and surrounding anatomy. A hierarchical attention architecture jointly encodes these streams to capture discriminative patterns of instrument use and instrument-anatomy interaction across surgeons at different training…