A Markerless 3D Tracking Framework for Continuum Surgical Tools Using a Surgical Tool Partial Pose Estimation Network Based on Domain Randomization

3D tracking of single-port continuum surgical tools is an essential step toward their closed-loop control in robot-assisted-laparoscopy, since single-port tools possess multiple degrees-of-freedom (DoFs) without distal joint sensors and hence have lower motion precision compared to rigid straight-stemmed tools used in multi-port robotic laparoscopy. This work proposes a novel markerless 3D tracking framework for continuum surgical tools using a proposed surgical tool partial pose estimation network (STPPE-Net) based on U-Net and ResNet. The STPPE-Net estimates the segmentation and a 5-DoF pose of the tool end-effector. This network is entirely trained by a synthetic data generator based on domain randomization (DR) and requires zero manual annotation. The 5-DoF pose estimation from the STPPE-Net is combined with the surgical tool axial rotation from the robot control system. Then, the entire pose is further refined via a region-based optimization that maximizes the overlap between the tool end-effector segmentation from the STPPE-Net and its projection onto the image plane of the endoscopic camera. The segmentation accuracy and 6-DoF pose estimation precision of the proposed framework are validated on the images captured from an endoscopic single-port system. The experimental results show the effectiveness and robustness of the proposed tracking framework for continuum surgical tools. This article proposes a new endoscopic-image-based markerless 3D tracking framework for continuum surgical tools. The framework consists of a surgical tool partial pose estimation network (STPPE-Net), a synthetic data generator, a kinematics module, and a region-based optimization module. The results demonstrate the framework's feasibility for use in the segmentation and 6-degrees-of-freedom (DoF) pose estimation of continuum surgical tools.image (c) 2024 WILEY-VCH GmbH