Surgical Suture Thread Predict Framework For Multi-Scenario Pose-Aware Based on Diffusion Probabilistic Model

Autonomous robot-assisted minimally invasive surgery is a key development direction to achieve precision medicine and reduce operator fatigue, while the core functions of existing surgical robotic systems are still confined to the master-slave remote-control mode, which makes it difficult to realize task autonomy for fine operations such as suturing and knotting. Especially in surgical suturing scenarios, real-time tracking and 3D reconstruction of suture lines face multiple challenges. To this end, we propose a suture line pose estimation method based on a diffusion probabilistic model. Considering the multi-source noise interference in the surgical scene, we first obtain the segmentation mask of the suture line and complement it as the feasible domain for the pose estimation, and add the start and end constraints to ensure the accuracy of the pose estimation. Subsequently, we model the task of resolving the geometric structure of the suture line as a path-planning problem under conditional constraints. The acquired feasible domains are encoded as global observations, and the suture trajectories are iteratively optimized by a denoising diffusion probabilistic model (DDPM), which ultimately leads to the pose estimation of the sutures. We conduct extensive experimental evaluations on a multi-scene dataset with sutures of different topologies, and the proposed method maintains the accuracy of the results in both non-critical occlusion and self-crossing cases. Based on this, further experiments on 3D reconstruction of sutures are performed and results are presented to validate the possibility of the present method to provide crucial a priori information for robotic surgical autonomy.