Renal surface reconstruction and segmentation for image-guided surgical navigation of laparoscopic partial nephrectomy.

An unpredictable dynamic surgical environment makes it necessary to measure morphological information of target tissue real-time for laparoscopic image-guided navigation. The stereo vision method for intraoperative tissue 3D reconstruction has the most potential for clinical development benefiting from its high reconstruction accuracy and laparoscopy compatibility. However, existing stereo vision methods have difficulty in achieving high reconstruction accuracy in real time. Also, intraoperative tissue reconstruction results often contain complex background and instrument information that prevents clinical development for image-guided systems. Taking laparoscopic partial nephrectomy (LPN) as the research object, this paper realizes a real-time dense reconstruction and extraction of the kidney tissue surface. The central symmetrical Census based semi-global block stereo matching algorithm is proposed to generate a dense disparity map. A GPU-based pixel-by-pixel connectivity segmentation mechanism is designed to segment the renal tissue area. An in-vitro porcine heart, in-vivo porcine kidney and offline clinical LPN data were performed to evaluate the accuracy and effectiveness of our approach. The algorithm achieved a reconstruction accuracy of ± 2 mm with a real-time update rate of 21 fps for an HD image size of 960 × 540, and 91.0% target tissue segmentation accuracy even with surgical instrument occlusions. Experimental results have demonstrated that the proposed method could accurately reconstruct and extract renal surface in real-time in LPN. The measurement results can be used directly for image-guided systems. Our method provides a new way to measure geometric information of target tissue intraoperatively in laparoscopy surgery. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-023-00263-1.

Accuracy Evaluation of a Novel Spinal Robotic System for Autonomous Laminectomy in Thoracic and Lumbar Vertebrae: A Cadaveric Study.

BACKGROUND: The main function of robots in spine surgery is to assist with pedicle screw placement. Laminectomy, which is as important as pedicle screw placement, lacks a mature robot-assisted system. The aims of this study were to introduce the first autonomous laminectomy robot, to explore the feasibility of autonomous robotic laminectomy, and to validate its accuracy using a cadaveric model. METHODS: Forty vertebrae from 4 cadavers were included in the study; 7 thoracic and 3 lumbar vertebrae were randomly selected in each cadaver. The surgeon was able to plan the laminectomy path based on computed tomographic (CT) data before the surgical procedure. The robot performed the laminectomy autonomously, and a postoperative CT scan was made. The deviation of each cutting plane from the plan was quantitatively analyzed, and the accuracy and safety were qualitatively evaluated. The time required for the laminectomy was also recorded. RESULTS: Cuts were performed in 80 laminectomy planes (56 for thoracic vertebrae and 24 for lumbar vertebrae). The mean time for 1-sided laminectomy was 333.59 ± 116.49 seconds, which was shorter for thoracic vertebrae (284.41 ± 66.04 seconds) than lumbar vertebrae (448.33 ± 128.65 seconds) (p < 0.001). The mean time for single-level total laminectomy was 814.05 ± 302.23 seconds, which was also shorter for thoracic vertebrae (690.46 ± 165.74 seconds) than lumbar vertebrae (1,102.42 ± 356.13 seconds) (p = 0.002). The mean deviation of the cutting plane from the plan was 0.67 ± 0.30 mm for the most superior cutting point and 0.73 ± 0.31 mm for the most inferior point. There were no significant differences in the deviation between thoracic vertebrae (0.66 ± 0.26 mm) and lumbar vertebrae (0.67 ± 0.38 mm) at the superior cutting point (p = 0.908) and between thoracic vertebrae (0.72 ± 0.30 mm) and lumbar vertebrae (0.73 ± 0.33 mm) at the inferior cutting point (p = 0.923). In the qualitative analysis of the accuracy of the 80 laminectomy planes, 66 (83%) were classified as grade A, 14 (18%) were grade B, and none was grade C. In the safety analysis, 65 planes (81%) were considered safe and the safety of the other 15 planes (19%) was considered uncertain. CONCLUSIONS: The results confirmed the accuracy of this robotic system, supporting its use for laminectomy of thoracolumbar vertebrae. LEVEL OF EVIDENCE: Therapeutic Level V . See Instructions for Authors for a complete description of levels of evidence.

A markerless automatic deformable registration framework for augmented reality navigation of laparoscopy partial nephrectomy.

Purpose Video see-through augmented reality (VST-AR) navigation for laparoscopic partial nephrectomy (LPN) can enhance intraoperative perception of surgeons by visualizing surgical targets and critical structures of the kidney tissue. Image registration is the main challenge in the procedure. Existing registration methods in laparoscopic navigation systems suffer from limitations such as manual alignment, invasive external marker fixation, relying on external tracking devices with bulky tracking sensors and lack of deformation compensation. To address these issues, we present a markerless automatic deformable registration framework for LPN VST-AR navigation. METHOD: Dense stereo matching and 3D reconstruction, automatic segmentation and surface stitching are combined to obtain a larger dense intraoperative point cloud of the renal surface. A coarse-to-fine deformable registration is performed to achieve a precise automatic registration between the intraoperative point cloud and the preoperative model using the iterative closest point algorithm followed by the coherent point drift algorithm. Kidney phantom experiments and in vivo experiments were performed to evaluate the accuracy and effectiveness of our approach. RESULTS: The average segmentation accuracy rate of the automatic segmentation was 94.9%. The mean target registration error of the phantom experiments was found to be 1.28 ± 0.68 mm (root mean square error). In vivo experiments showed that tumor location was identified successfully by superimposing the tumor model on the laparoscopic view. CONCLUSION: Experimental results have demonstrated that the proposed framework could accurately overlay comprehensive preoperative models on deformable soft organs automatically in a manner of VST-AR without using extra intraoperative imaging modalities and external tracking devices, as well as its potential clinical use.

Intensity-based 2D-3D registration for an ACL reconstruction navigation system.

To improve the positioning accuracy of tunnels for anterior cruciate ligament (ACL) reconstruction, we proposed an intensity-based 2D-3D registration method for an ACL reconstruction navigation system. Methods for digitally reconstructed radiograph (DRR) generation, similarity measurement, and optimization are crucial to 2D-3D registration. We evaluated the accuracy, success rate, and processing time of different methods: (a) ray-casting and splating were compared for DRR generation; (b) normalized mutual information (NMI), Mattes mutual information (MMI), and Spearman's rank correlation coefficient (SRC) were assessed for similarity between registrations; and (c) gradient descent (GD) and downhill simplex (DS) were compared for optimization. The combination of splating, SRC, and GD provided the best composite performance and was applied in an augmented reality (AR) ACL reconstruction navigation system. The accuracy of the navigation system could fulfill the clinical needs of ACL reconstruction, with an end pose error of 2.50 mm and an angle error of 2.74°.