Marker-free surgical navigation of rod bending using a stereo neural network and augmented reality in spinal fusion

The instrumentation of spinal fusion surgeries includes pedicle screw placement and rod implantation. While several surgical navigation approaches have been proposed for pedicle screw placement, less attention has been devoted towards the guidance of patient-specific adaptation of the rod implant. We propose a marker-free and intuitive Augmented Reality (AR) approach to navigate the bending process required for rod implantation. A stereo neural network is trained from the stereo video streams of the Microsoft HoloLens in an end-to-end fashion to determine the location of corresponding pedicle screw heads. From the digitized screw head positions, the optimal rod shape is calculated, translated into a set of bending parameters, and used for guiding the surgeon with a novel navigation approach. In the AR-based navigation, the surgeon is guided step-by-step in the use of the surgical tools to achieve an optimal result. We have evaluated the performance of our method on human cadavers against two benchmark methods, namely conventional freehand bending and marker-based bending navigation in terms of bending time and rebending maneuvers. We achieved an average bending time of 231s with 0.6 rebending maneuvers per rod compared to 476s (3.5 rebendings) and 348s (1.1 rebendings) obtained by our freehand and marker-based benchmarks, respectively

Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted Retinal Surgery

Computer vision and robotics are being increasingly applied in medical interventions. Especially in interventions where extreme precision is required they could make a difference. One such application is robot-assisted retinal microsurgery. In recent works, such interventions are conducted under a stereo-microscope, and with a robot-controlled surgical tool. The complementarity of computer vision and robotics has however not yet been fully exploited. In order to improve the robot control we are interested in 3D reconstruction of the anatomy and in automatic tool localization using a stereo microscope. In this paper, we solve this problem for the first time using a single pipeline, starting from uncalibrated cameras to reach metric 3D reconstruction and registration, in retinal microsurgery. The key ingredients of our method are: (a) surgical tool landmark detection, and (b) 3D reconstruction with the stereo microscope, using the detected landmarks. To address the former, we propose a novel deep learning method that detects and recognizes keypoints in high definition images at higher than real-time speed. We use the detected 2D keypoints along with their corresponding 3D coordinates obtained from the robot sensors to calibrate the stereo microscope using an affine projection model. We design an online 3D reconstruction pipeline that makes use of smoothness constraints and performs robot-to-camera registration. The entire pipeline is extensively validated on open-sky porcine eye sequences. Quantitative and qualitative results are presented for all steps.status: publishe

Visual Tracking in Robotic Minimally Invasive Surgery

Intra-operative imaging and robotics are some of the technologies driving forward better and more effective minimally invasive surgical procedures. To advance surgical practice and capabilities further, one of the key requirements for computationally enhanced interventions is to know how instruments and tissues move during the operation. While endoscopic video captures motion, the complex appearance dynamic effects of surgical scenes are challenging for computer vision algorithms to handle with robustness. Tackling both tissue and instrument motion estimation, this thesis proposes a combined non-rigid surface deformation estimation method to track tissue surfaces robustly and in conditions with poor illumination. For instrument tracking, a keypoint based 2D tracker that relies on the Generalized Hough Transform is developed to initialize a 3D tracker in order to robustly track surgical instruments through long sequences that contain complex motions. To handle appearance changes and occlusion a patch-based adaptive weighting with segmentation and scale tracking framework is developed. It takes a tracking-by-detection approach and a segmentation model is used to assigns weights to template patches in order to suppress back- ground information. The performance of the method is thoroughly evaluated showing that without any offline-training, the tracker works well even in complex environments. Finally, the thesis proposes a novel 2D articulated instrument pose estimation framework, which includes detection-regression fully convolutional network and a multiple instrument parsing component. The framework achieves compelling performance and illustrates interesting properties includ- ing transfer between different instrument types and between ex vivo and in vivo data. In summary, the thesis advances the state-of-the art in visual tracking for surgical applications for both tissue and instrument motion estimation. It contributes to developing the technological capability of full surgical scene understanding from endoscopic video