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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

28.10.2018 | Original Article

Robust navigation support in lowest dose image setting

verfasst von: Mai Bui, Felix Bourier, Christoph Baur, Fausto Milletari, Nassir Navab, Stefanie Demirci

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 2/2019

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Abstract

Purpose

Clinical cardiac electrophysiology (EP) is concerned with diagnosis and treatment of cardiac arrhythmia describing abnormality or perturbation in the normal activation sequence of the myocardium. With the recent introduction of lowest dose X-ray imaging protocol for EP procedures, interventional image enhancement has gained crucial importance for the well-being of patients as well as medical staff.

Methods

In this paper, we introduce a novel method to detect and track different EP catheter electrodes in lowest dose fluoroscopic sequences based on \(\ell _1\)-sparse coding and online robust PCA (ORPCA). Besides being able to work on real lowest dose sequences, the underlying methodology achieves simultaneous detection and tracking of three main EP catheters used during ablation procedures.

Results

We have validated our algorithm on 16 lowest dose fluoroscopic sequences acquired during real cardiac ablation procedures. In addition to expert labels for 2 sequences, we have employed a crowdsourcing strategy to obtain ground truth labels for the remaining 14 sequences. In order to validate the effect of different training data, we have employed a leave-one-out cross-validation scheme yielding an average detection rate of \(86.9\%\).

Conclusion

Besides these promising quantitative results, our medical partners also expressed their high satisfaction. Being based on \(\ell _1\)-sparse coding and online robust PCA (ORPCA), our method advances previous approaches by being able to detect and track electrodes attached to multiple different catheters.

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Annot8: http://vmnavab14.informatik.tu-muenchen.de/.

A popular crowdsourcing platform for data science and machine learning.

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Titel: Robust navigation support in lowest dose image setting
verfasst von: Mai Bui
Felix Bourier
Christoph Baur
Fausto Milletari
Nassir Navab
Stefanie Demirci
Publikationsdatum: 28.10.2018
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 2/2019
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-018-1874-8

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Abstract

Purpose

Methods

Results

Conclusion

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