Self-Supervised Cyclic Diffeomorphic Mapping for Soft Tissue Deformation Recovery in Robotic Surgery Scenes

The ability to recover tissue deformation from surgical video is fundamental for many downstream applications in robotic surgery. Despite noticeable advancements, this task remains under-explored due to the complex dynamics of soft tissues manipulated by surgical instruments. Achieving dense and accurate tissue tracking is further complicated by ambiguous pixel correspondence in regions with homogeneous texture. In this paper, we introduce a novel self-supervised framework to recover tissue deformations from stereo surgical videos. Our approach integrates semantics, cross-frame motion flow, and long-range temporal dependencies to accurately represent tissue dynamics for deformation recovery. Moreover, we incorporate diffeomorphic mapping to regularize the warping field to be physically more realistic. To comprehensively evaluate our method, we collected stereo surgical video clips containing three types of tissue manipulation (i.e., pushing, dissection and retraction) from two surgical procedures (i.e., hemicolectomy and mesorectal excision). Our method demonstrates promising results in capturing tissue 3D deformation, and generalizes well across different actions and procedures. It also outperforms current state-of-the-art approaches based on non-rigid registration and optical flow estimation. To the best of our knowledge, this is the first work on self-supervised learning for dense tissue deformation modeling from stereo surgical videos. The paper's code is available at: https://github.com/ med-air/RecoverTissueDeform.