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

Chang, Ping-Lin; Rolls, Alexander; Praetere, Herbert De; Poorten, Emmanuel Vander; Riga, Celia; Bicknell, Colin; Stoyanov, Danail

doi:10.1109/lra.2016.2517821

Cited by 31 publications

(28 citation statements)

References 17 publications

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“…blob detection, shape constrained searching and model-/templatebased detection ( [25,24]). [8] modeled the catheter tracking problem by optimizing the posterior in a Bayesian framework, in which the catheter was represented as a B-spline tube model and was tracked by fitting the B-spline to measurements based on gray intensity and vesselness image. [5] proposed a convolutional neural network (CNN) to detect catheter electrodes in X-ray images, which treated catheter detection as a segmentation problem.…”

Section: Interventional / Surgical Tool Trackingmentioning

confidence: 99%

Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering

Smal

Daemen

et al. 2020

Medical Image Analysis

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Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified with X-ray opaque contrast agents. Interventional cardiologists typically navigate instruments using non-contrast-enhanced fluoroscopic images, since higher use of contrast agents increases the risk of kidney failure. When using fluoroscopic images, the interventional cardiologist needs to rely on a mental anatomical reconstruction. This paper reports on the development of a novel dynamic coronary roadmapping approach for improving visual feedback and reducing contrast use during PCI. The approach compensates cardiac and respiratory induced vessel motion by ECG alignment and catheter tip tracking in X-ray fluoroscopy, respectively. In particular, for accurate and robust tracking of the catheter tip, we proposed a new deep learning based Bayesian filtering method that integrates the detection outcome of a convolutional neural network and the motion estimation between frames using a particle filtering framework. The proposed roadmapping and tracking approaches were validated on clinical X-ray images, achieving accurate performance on both catheter tip tracking and dynamic coronary roadmapping experiments. In addition, our approach runs in real-time on a computer with a single GPU and has the potential to be integrated into the clinical workflow of PCI procedures, providing cardiologists with visual guidance during interventions without the need of extra use of contrast agent.

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Section: Interventional / Surgical Tool Trackingmentioning

confidence: 99%

Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering

Smal

Daemen

et al. 2020

Medical Image Analysis

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“…Note that the results, the accuracy metrics and computation times cannot be directly compared but they give an idea of the performances. [17] No 500 ms mean 2 px (0.4 mm) mean 5.4 px 2012 Heibel et al [7] No 60 ms mean 0.8-3.9 px -2016 Chang et al [4] No ---2016 Chen et al [5] No -mean 2.1 px (0.5 mm) -2016 Wagner et al [16] Yes > 1 min mean 0.5 mm -This work…”

Section: Introductionmentioning

confidence: 91%

“…These methods have two drawbacks: the first frame of the fluoroscopic sequence has to be manually annotated and the curvature and length of the catheter should not change much between frames. [4,7] propose semi-automatic methods to segment the first frame. Recently, a fully automatic method using directional noise reduction and path extraction, with segments and similarity from the previous frame cost function, has been proposed [16].…”

Section: Introductionmentioning

confidence: 99%

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

Ambrosini

Ruijters

Niessen

et al. 2017

Lecture Notes in Computer Science

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Abstract. 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.

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“…Following, the location of the catheter is extracted in each frame by fitting and then optimizing a model of the instrument, which for catheters/guidewires is normally a spline parameterisation of their shape. 87 A number of approaches choose to only track the distal point (tip), as this is deemed the most important point of the catheter/guidewire. [88][89][90] A plethora of different processing methods based on machine learning, filtering and template matching, Hidden Markov Models (HMM), has been proposed for the tracking of catheters/guidewires and other surgical instruments (e.g.…”

Section: Tracking Instruments In Fluoroscopymentioning

confidence: 99%

“…89,[91][92][93][94][95][96][97] Recently a number of methods have been developed for tracking the full shape of catheters/guidewires in fluoroscopy. 87,96,97 Three different approaches to achieve this are illustrated in Fig. 3.…”

Section: Tracking Instruments In Fluoroscopymentioning

confidence: 99%

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

Mazomenos

Chang

Rolls

et al. 2016

J. Med. Robot. Res.

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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 recognized 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.

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

Cited by 31 publications

References 17 publications

Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering

Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering

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

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

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