Hierarchical Learning of Curves Application to Guidewire Localization in Fluoroscopy

Barbu, Adrian; Athitsos, Vassilis; Georgescu, Bogdan; Boehm, Stephanie; Durlak, Peter; Comanicìu, Dorin

doi:10.1109/cvpr.2007.383033

Cited by 44 publications

(38 citation statements)

References 9 publications

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“…Various methods for catheter tracking have been proposed. They involve filter-based techniques [5] as well as template-matching and learning-based approaches [6]. For electrophysiology procedures, different types of catheters are available.…”

Section: Motivationmentioning

confidence: 99%

Catheter Tracking: Filter-Based vs. Learning-Based

Brost

Wimmer

Liao

et al. 2010

Lecture Notes in Computer Science

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Abstract. Atrial fibrillation is the most common sustained arrhythmia. One important treatment option is radio-frequency catheter ablation (RFCA) of the pulmonary veins attached to the left atrium. RFCA is usually performed under fluoroscopic (X-ray) image guidance. Overlay images computed from pre-operative 3-D volumetric data can be used to add anatomical detail otherwise not visible under X-ray. Unfortunately, current fluoro overlay images are static, i.e., they do not move synchronously with respiratory and cardiac motion. A filter-based catheter tracking approach using simultaneous biplane fluoroscopy was previously presented. It requires localization of a circumferential tracking catheter, though. Unfortunately, the initially proposed method may fail to accommodate catheters of different size. It may also detect wrong structures in the presence of high background clutter. We developed a new learning-based approach to overcome both problems. First, a 3-D model of the catheter is reconstructed. A cascade of boosted classifiers is then used to segment the circumferential mapping catheter. Finally, the 3-D motion at the site of ablation is estimated by tracking the reconstructed model in 3-D from biplane fluoroscopy. We compared our method to the previous approach using 13 clinical data sets and found that the 2-D tracking error improved from 1.0 mm to 0.8 mm. The 3-D tracking error was reduced from 0.8 mm to 0.7 mm.

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Section: Motivationmentioning

confidence: 99%

Catheter Tracking: Filter-Based vs. Learning-Based

Brost

Wimmer

Liao

et al. 2010

Lecture Notes in Computer Science

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“…Such procedures are minimally invasive and have been used increasingly often in recent years, in areas ranging from coronary angioplasty to tumor embolization. Over the past years, guide-wire detection in fluoroscopic images has gained interest and maturity among the image processing community [1,2,3,4]. A large number of applications rely upon its characteristics, such as visualization enhancement, 3D guide-wire reconstruction and respiratory motion tracking.…”

Section: Guide-wire Detection In X-ray Fluoroscopymentioning

confidence: 99%

“…Curvilinear structure segmentation techniques and particularly guide-wire detection in X-ray fluoroscopy is often presented as a 3-step pipeline: (1) The local building of a feature map representing the probability of presence of an elongated structure at each pixel [2,3,4,5,6]. Its computation often involves considering the neighborhood of each pixel, on which structures are assumed to be straight.…”

Section: State Of the Artmentioning

confidence: 99%

Curvilinear Structure Enhancement with the Polygonal Path Image - Application to Guide-Wire Segmentation in X-Ray Fluoroscopy

Bismuth

Vaillant

Talbot

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Curvilinear structures are common in medical imaging, which typically require dedicated processing techniques. We present a new structure to process these, that we call the polygonal path image, denoted P. We derive from P some curvilinear structure enhancement and analysis algorithms. We show that P has some interesting properties: it generalizes several concepts found in other methods; it makes it possible to control the smoothness and length of the structures under study; and it can be computed efficiently. We estimate quantitatively its performance in the context of interventional cardiology for the detection of guide-wires in Xray images. We show that P is particularly well suited for this task where it appears to outperform previous state of the art techniques.

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“…The hierarchical data organization presented in this article has the main scale-space properties studied in [6]. Early attempt for using hierarchical data organization for guidewire localization has been made by Barbu et al [1]. They use marginal space learning based hierarchical model of curves (obtained from low-level segment detector) to model complex free-form curves.…”

Section: Introductionmentioning

confidence: 99%

“…The similarity with our approach is that both are bottom up approaches with low level segment/blob detector as first step. Though [1] does not show segmentation of empty catheter, a head-to-head comparison would be helpful but neither the dataset nor the implementation has been made public. Reported computational time are close to ours.…”

Section: Introductionmentioning

confidence: 99%

Scale-space for empty catheter segmentation in PCI fluoroscopic images

Bacchuwar

Cousty

Vaillant³

et al. 2017

Int J CARS

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Purpose: In this article, we present a method for empty guiding catheter segmentation in fluoroscopic X-ray images. The guiding catheter, being a commonly visible landmark, its segmentation is an important and a difficult brick for Percutaneous Coronary Intervention (PCI) procedure modeling. Methods: In number of clinical situations, the catheter is empty and appears as a low contrasted structure with two parallel and partially disconnected edges. To segment it, we work on the level-set scale-space of image, the min tree, to extract curve blobs. We then propose a novel structural scale-space, a hierarchy built on these curve blobs. The deep connected component, i.e. the cluster of curve blobs on this hierarchy, that maximizes the likelihood to be an empty catheter is retained as final segmentation. Results: We evaluate the performance of the algorithm on a database of 1250 fluoroscopic images from 6 patients. As a result, we obtain very good qualitative and quantitative segmentation performance, with mean precision and recall of 80.48% and 63.04% respectively. Conclusions: We develop a novel structural scale-space to segment a structured object, the empty catheter, in challenging situations where the information content is very sparse in the images. Fully-automatic empty catheter segmentation in X-ray fluoroscopic images is an important and preliminary step in PCI procedure modeling, as it aids in tagging the arrival and removal location of other interventional tools.

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Hierarchical Learning of Curves Application to Guidewire Localization in Fluoroscopy

Cited by 44 publications

References 9 publications

Catheter Tracking: Filter-Based vs. Learning-Based

Catheter Tracking: Filter-Based vs. Learning-Based

Curvilinear Structure Enhancement with the Polygonal Path Image - Application to Guide-Wire Segmentation in X-Ray Fluoroscopy

Scale-space for empty catheter segmentation in PCI fluoroscopic images

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