Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy

Augmenting X-ray imaging with 3D roadmap to improve guidance is a common strategy. Such approaches benefit from automated analysis of the X-ray images, such as the automatic detection and tracking of instruments. In this paper, we propose a real-time method to segment the catheter and guidewire in 2D X-ray fluoroscopic sequences. The method is based on deep convolutional neural networks. The network takes as input the current image and the three previous ones, and segments the catheter and guidewire in the current image. Subsequently, a centerline model of the catheter is constructed from the segmented image. A small set of annotated data combined with data augmentation is used to train the network. We trained the method on images from 182 X-ray sequences from 23 different interventions. On a testing set with images of 55 X-ray sequences from 5 other interventions, a median centerline distance error of 0.2 mm and a median tip distance error of 0.9 mm was obtained. The segmentation of the instruments in 2D X-ray sequences is performed in a real-time fully-automatic manner.Comment: Accepted to MICCAI 201

A Survey on the Current Status and Future Challenges Towards Objective Skills Assessment in Endovascular Surgery

Minimally-invasive endovascular interventions have evolved rapidly over the past decade, facilitated by breakthroughs in medical imaging and sensing, instrumentation and most recently robotics. Catheter based operations are potentially safer and applicable to a wider patient population due to the reduced comorbidity. As a result endovascular surgery has become the preferred treatment option for conditions previously treated with open surgery and as such the number of patients undergoing endovascular interventions is increasing every year. This fact coupled with a proclivity for reduced working hours, results in a requirement for efficient training and assessment of new surgeons, that deviates from the “see one, do one, teach one” model introduced by William Halsted, so that trainees obtain operational expertise in a shorter period. Developing more objective assessment tools based on quantitative metrics is now a recognised need in interventional training and this manuscript reports the current literature for endovascular skills assessment and the associated emerging technologies. A systematic search was performed on PubMed (MEDLINE), Google Scholar, IEEXplore and known journals using the keywords, “endovascular surgery”, “surgical skills”, “endovascular skills”, “surgical training endovascular” and “catheter skills”. Focusing explicitly on endovascular surgical skills, we group related works into three categories based on the metrics used; structured scales and checklists, simulation-based and motion-based metrics. This review highlights the key findings in each category and also provides suggestions for new research opportunities towards fully objective and automated surgical assessment solutions

Robust Catheter and Guidewire Tracking Using B-spline Tube Model and Pixel-Wise Posteriors

In endovascular surgery and cardiology, robotic catheters are emerging as a promising technology for enhanced catheter manipulation and navigation while reducing radiation exposure. For robotic catheter systems especially with tendon actuation, a key challenge is the localisation of the catheter shape and position within the anatomy. An effective approach is through image-based catheter/guidewire detection and tracking. However, these are difficult problems due to the thin appearance of the instruments in the image and the low signal-to-noise ratio of fluoroscopy. In this paper, we propose a deformable B-spline tube model which can effectively represent the shape of a catheter and guidewire. The model allows fitting using a region-based probabilistic algorithm which does not rely on intensity gradients but exploits a signed distance function and the non-parametric distributions of measurements. Unlike previous B-spline fitting approaches which optimise the spline with respect to control points, we propose a knot-driven scheme with an equidistance prior in order to better fit complex curves. Our probabilistic framework shows promising results for catheter and guidewire tracking in different procedures even with handling overlapping instrument segments. We present empirical studies using phantom model data and in vivo fluoroscopic sequences with annotated ground truth. Our results indicate that the proposed approach can precisely model the catheter and guidewire contours in near real time, and this information can be embedded in a robotic catheter control loop or utilised for image-guidance.status: publishe

Improved Image Guidance in TACE Procedures

Purpose of the work in this thesis is to improve the image guidance in TACE procedures. More specifically, we intend to develop and evaluate technology that permits dynamic roadmapping based on a 3D model of the liver vasculature

Dynamic Analysis of X-ray Angiography for Image-Guided Coronary Interventions

Percutaneous coronary intervention (PCI) is a minimally-invasive procedure for treating patients with coronary artery disease. PCI is typically performed with image guidance using X-ray angiograms (XA) in which coronary arter