Towards Occlusion-Aware Pose Estimation of Surgical Suturing Threads

The technique of robust suture detection is vital in many applications including the skill evaluation for trainee, and suture augmentation in robotic-assisted surgery. Due to the complicated environment in surgery, the pose estimation of suture threads is challenged by the foreground and the background occlusion.

To address this problem, we proposed an Occlusion-Aware Spatial Propagation model in this work. The challenging cases caused by self-intersection of threads are resolved by modeling the region connectivity. By taking the advantage of context-information, the background-occlusion is handled with the guided spatial propagation mechanism.

Experiments on phantom and ex-vivo datasets demonstrate the proposed method achieves superior accuracy on pose estimation compared to baseline methods, indicating the effectiveness of the occlusion-aware connectivity and spatial propagation.

Our proposed method provides a general framework for fully end-to-end pose estimation of suturing thread that achieves promising quality without the external simulation.

Our fully automated algorithm addresses the occlusion problem including foreground and background occlusion which are common in surgery, and we anticipate that it will substantially provide the prior for future autonomy of robotic surgery.