Comparison of manual versus robot-assisted contralateral gate cannulation in patients undergoing endovascular aneurysm repair.

PURPOSE: Robotic endovascular technology may offer advantages over conventional manual catheter techniques. Our aim was to compare the endovascular catheter path-length (PL) for robotic versus manual contralateral gate cannulation during endovascular aneurysm repair (EVAR), using video motion analysis (VMA). METHODS: This was a multicentre retrospective cohort study with fluoroscopic video recordings of 24 EVAR cases (14 robotic, 10 manual) performed by experienced operators (> 50 procedures), obtained from four leading European centres. Groups were comparable with no statistically significant differences in aneurysm size (p = 0.47) or vessel tortuosity (p = 0.68). Two trained assessors used VMA to calculate the catheter PL during contralateral gate cannulation for robotic versus manual approaches. RESULTS: There was a high degree of inter-observer reliability (Cronbach's α > 0.99) for VMA. Median robotic PL was 35.7 cm [interquartile range, IQR (30.8-51.0)] versus 74.1 cm [IQR (44.3-170.4)] for manual cannulation, p = 0.019. Robotic cases had a median cannulation time of 5.33 min [IQR (4.58-6.49)] versus 1.24 min [IQR (1.13-1.35)] in manual cases (p = 0.0083). Generated efficiency ratios (PL/aorto-iliac centrelines) was 1.6 (1.2-2.1) in robotic cases versus 2.6 (1.7-7.0) in manual, p = 0.031. CONCLUSION: Robot-assisted contralateral gate cannulation in EVAR leads to decreased navigation path lengths and increased economy of movement compared with manual catheter techniques. The benefit could be maximised by prioritising robotic catheter shaping over habituated reliance on guidewire manipulation. Robotic technology has the potential to reduce the endovascular footprint during manipulations even for experienced operators with the added advantage of zero radiation exposure.

Articulated Multi-Instrument 2-D Pose Estimation Using Fully Convolutional Networks.

Instrument detection, pose estimation, and tracking in surgical videos are an important vision component for computer-assisted interventions. While significant advances have been made in recent years, articulation detection is still a major challenge. In this paper, we propose a deep neural network for articulated multi-instrument 2-D pose estimation, which is trained on detailed annotations of endoscopic and microscopic data sets. Our model is formed by a fully convolutional detection-regression network. Joints and associations between joint pairs in our instrument model are located by the detection subnetwork and are subsequently refined through a regression subnetwork. Based on the output from the model, the poses of the instruments are inferred using maximum bipartite graph matching. Our estimation framework is powered by deep learning techniques without any direct kinematic information from a robot. Our framework is tested on single-instrument RMIT data, and also on multi-instrument EndoVis and in vivo data with promising results. In addition, the data set annotations are publicly released along with our code and model.

Catheter manipulation analysis for objective performance and technical skills assessment in transcatheter aortic valve implantation.

PURPOSE: Transcatheter aortic valve implantation (TAVI) demands precise and efficient handling of surgical instruments within the confines of the aortic anatomy. Operational performance and dexterous skills are critical for patient safety, and objective methods are assessed with a number of manipulation features, derived from the kinematic analysis of the catheter/guidewire in fluoroscopy video sequences. METHODS: A silicon phantom model of a type I aortic arch was used for this study. Twelve endovascular surgeons, divided into two experience groups, experts ([Formula: see text]) and novices ([Formula: see text]), performed cannulation of the aorta, representative of valve placement in TAVI. Each participant completed two TAVI experiments, one with conventional catheters and one with the Magellan robotic platform. Video sequences of the fluoroscopic monitor were recorded for procedural processing. A semi-automated tracking software provided the 2D coordinates of the catheter/guidewire tip. In addition, the aorta phantom was segmented in the videos and the shape of the entire catheter was manually annotated in a subset of the available video frames using crowdsourcing. The TAVI procedure was divided into two stages, and various metrics, representative of the catheter's overall navigation as well as its relative movement to the vessel wall, were developed. RESULTS: Experts consistently exhibited lower values of procedure time and dimensionless jerk, and higher average speed and acceleration than novices. Robotic navigation resulted in increased average distance to the vessel wall in both groups, a surrogate measure of safety and reduced risk of embolisation. Discrimination of experience level and types of equipment was achieved with the generated motion features and established clustering algorithms. CONCLUSIONS: Evaluation of surgical skills is possible through the analysis of the catheter/guidewire motion pattern. The use of robotic endovascular platforms seems to enable more precise and controlled catheter navigation.

Modeling of the bony pelvis from MRI using a multi-atlas AE-SDM for registration and tracking in image-guided robotic prostatectomy.

A fundamental challenge in the development of image-guided surgical systems is alignment of the preoperative model to the operative view of the patient. This is achieved by finding corresponding structures in the preoperative scans and on the live surgical scene. In robot-assisted laparoscopic prostatectomy (RALP), the most readily visible structure is the bone of the pelvic rim. Magnetic resonance imaging (MRI) is the modality of choice for prostate cancer detection and staging, but extraction of bone from MRI is difficult and very time consuming to achieve manually. We present a robust and fully automated multi-atlas pipeline for bony pelvis segmentation from MRI, using a MRI appearance embedding statistical deformation model (AE-SDM). The statistical deformation model is built using the node positions of deformations obtained from hierarchical registrations of full pelvis CT images. For datasets with corresponding CT and MRI images, we can transform the MRI into CT SDM space. MRI appearance can then be used to improve the combined MRI/CT atlas to MRI registration using SDM constraints. We can use this model to segment the bony pelvis in a new MRI image where there is no CT available. A multi-atlas segmentation algorithm is introduced which incorporates MRI AE-SDMs guidance. We evaluated the method on 19 subjects with corresponding MRI and manually segmented CT datasets by performing a leave-one-out study. Several metrics are used to quantify the overlap between the automatic and manual segmentations. Compared to the manual gold standard segmentations, our robust segmentation method produced an average surface distance 1.24±0.27mm, which outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. We also show that the resulting surface can be tracked in the endoscopic view in near real time using dense visual tracking methods. Results are presented on a simulation and a real clinical RALP case. Tracking is accurate to 0.13mm over 700 frames compared to a manually segmented surface. Our method provides a realistic and robust framework for intraoperative alignment of a bony pelvis model from diagnostic quality MRI images to the endoscopic view.

Real-time dense stereo reconstruction using convex optimisation with a cost-volume for image-guided robotic surgery.

Reconstructing the depth of stereo-endoscopic scenes is an important step in providing accurate guidance in robotic-assisted minimally invasive surgery. Stereo reconstruction has been studied for decades but remains a challenge in endoscopic imaging. Current approaches can easily fail to reconstruct an accurate and smooth 3D model due to textureless tissue appearance in the real surgical scene and occlusion by instruments. To tackle these problems, we propose a dense stereo reconstruction algorithm using convex optimisation with a cost-volume to efficiently and effectively reconstruct a smooth model while maintaining depth discontinuity. The proposed approach has been validated by quantitative evaluation using simulation and real phantom data with known ground truth. We also report qualitative results from real surgical images. The algorithm outperforms state of the art methods and can be easily parallelised to run in real-time on recent graphics hardware.

Identifying regions of interest in medical images using self-organizing maps.

Advances in data acquisition, processing and visualization techniques have had a tremendous impact on medical imaging in recent years. However, the interpretation of medical images is still almost always performed by radiologists. Developments in artificial intelligence and image processing have shown the increasingly great potential of computer-aided diagnosis (CAD). Nevertheless, it has remained challenging to develop a general approach to process various commonly used types of medical images (e.g., X-ray, MRI, and ultrasound images). To facilitate diagnosis, we recommend the use of image segmentation to discover regions of interest (ROI) using self-organizing maps (SOM). We devise a two-stage SOM approach that can be used to precisely identify the dominant colors of a medical image and then segment it into several small regions. In addition, by appropriately conducting the recursive merging steps to merge smaller regions into larger ones, radiologists can usually identify one or more ROIs within a medical image.