Automatic Detection of Multiple and Overlapping EP Catheters in Fluoroscopic Sequences

Milletarì, Fausto; Navab, Nassir; Fallavollita, Pascal

doi:10.1007/978-3-642-40760-4_47

Cited by 15 publications

(17 citation statements)

References 17 publications

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“…As a further pre-processing step, we use a determinant of hessian blob detector to obtain the accurate location of electrode-like structures appearing in the images. As demonstrated by [13,14], the electrodes can in this way be localized with submillimeter precision, therefore enabling us to effectively limit the search space.…”

Section: Pre-processingmentioning

confidence: 96%

“…There, authors require user interaction for their detections using a geodesic framework. Finally, methods including fast blob detections, clustering, shape-constrained searching and catheter model-based detection have been proposed [13,14]. A limitation of these is that they assume fixed shape for the catheter and might not cope with different C-arm positions and catheter shape changes due to foreshortening.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fully Automatic Catheter Localization in C-Arm Images Using ℓ1-Sparse Coding

Milletarì

Belagiannis

Navab

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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Abstract. We propose a method to perform automatic detection and tracking of electrophysiology (EP) catheters in C-arm fluoroscopy sequences. Our approach does not require any initialization, is completely automatic, and can concurrently track an arbitrary number of overlapping catheters. After a pre-processing step, we employ sparse coding to first detect candidate catheter tips, and subsequently detect and track the catheters. The proposed technique is validated on 2835 C-arm images, which include 39,690 manually selected ground-truth catheter electrodes. Results demonstrated sub-millimeter detection accuracy and real-time tracking performances.

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Section: Pre-processingmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Fully Automatic Catheter Localization in C-Arm Images Using ℓ1-Sparse Coding

Milletarì

Belagiannis

Navab

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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“…Throughout the past years, there has been a lot of efforts towards detecting and tracking catheter tips in fluoroscopic sequences for various applications and purposes [12,14,15]. However, these techniques can not be applied for the stent graft delivery device due to its inhomogeneous appearance and rather large size.…”

Section: Related Workmentioning

confidence: 98%

Online tracking of interventional devices for endovascular aortic repair

et al. 2015

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“…Most of these tools are highly contrasted and relatively easy to segment. Milletari [13] showed that it's possible to segment EP catheters with segmentation accuracy of 99.3%. Brost et.…”

Section: Introductionmentioning

confidence: 99%

“…al [2] has also proposed efficient catheter tracking for electrophysiology procedures. Various works on segmentation of pigtail catheters [10] and EP catheters [12,13] portray the importance of segmentation of interventional tools and endovascular devices.…”

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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Automatic Detection of Multiple and Overlapping EP Catheters in Fluoroscopic Sequences

Cited by 15 publications

References 17 publications

Fully Automatic Catheter Localization in C-Arm Images Using ℓ1-Sparse Coding

Fully Automatic Catheter Localization in C-Arm Images Using ℓ1-Sparse Coding

Online tracking of interventional devices for endovascular aortic repair

Scale-space for empty catheter segmentation in PCI fluoroscopic images

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