Catheter segmentation in three-dimensional ultrasound images by feature fusion and model fitting

H. Yang (Corresponding author), C. Shan, A. Pourtaherian, Alexander F. Kolen, P.H.N. de With

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

Ultrasound (US) has been increasingly used during interventions, such as cardiac catheterization. To accurately identify the catheter inside US images, extra training for physician and sonographer are needed. As a consequence, automated segmentation of the catheter in US images and optimized presentation viewing to the physician can be beneficial to accelerate the efficiency and safety of interventions and improve their outcome. For cardiac catheterization, 3D US image is potentially attractive because of no radiation modality and richer spatial information. However, due to a limited spatial resolution of 3D cardiac US and complex anatomical structures inside the heart, image-based catheter segmentation is challenging. In this paper, we propose a cardiac catheter segmentation method in 3D US data through image processing techniques. Our method first applies a voxel-based classification through newly designed multi-scale and multi-definition features, which provide a robust catheter voxel segmentation in 3D US. Second, a modified catheter model fitting is applied to segment the curved catheter in 3D US images. The proposed method is validated with extensive experiments, using different in-vitro, ex-vivo and in-vivo datasets. The proposed method can segment the catheter within an average tip-point error that is smaller than the catheter diameter (1.9 mm) in the volumetric images. Based on automated catheter segmentation and combined with optimal viewing, physicians do not have to interpret US images and can focus on the procedure itself to improve the quality of cardiac intervention.
LanguageEnglish
Article number015001
Number of pages13
JournalJournal of Medical Imaging
Volume6
Issue number1
DOIs
StatePublished - 14 Jan 2019

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Three-Dimensional Imaging
Catheters
Cardiac Catheterization
Physicians
Cardiac Catheters
Radiation
Safety

Cite this

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title = "Catheter segmentation in three-dimensional ultrasound images by feature fusion and model fitting",
abstract = "Ultrasound (US) has been increasingly used during interventions, such as cardiac catheterization. To accurately identify the catheter inside US images, extra training for physician and sonographer are needed. As a consequence, automated segmentation of the catheter in US images and optimized presentation viewing to the physician can be beneficial to accelerate the efficiency and safety of interventions and improve their outcome. For cardiac catheterization, 3D US image is potentially attractive because of no radiation modality and richer spatial information. However, due to a limited spatial resolution of 3D cardiac US and complex anatomical structures inside the heart, image-based catheter segmentation is challenging. In this paper, we propose a cardiac catheter segmentation method in 3D US data through image processing techniques. Our method first applies a voxel-based classification through newly designed multi-scale and multi-definition features, which provide a robust catheter voxel segmentation in 3D US. Second, a modified catheter model fitting is applied to segment the curved catheter in 3D US images. The proposed method is validated with extensive experiments, using different in-vitro, ex-vivo and in-vivo datasets. The proposed method can segment the catheter within an average tip-point error that is smaller than the catheter diameter (1.9 mm) in the volumetric images. Based on automated catheter segmentation and combined with optimal viewing, physicians do not have to interpret US images and can focus on the procedure itself to improve the quality of cardiac intervention.",
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Catheter segmentation in three-dimensional ultrasound images by feature fusion and model fitting. / Yang, H. (Corresponding author); Shan, C.; Pourtaherian, A.; Kolen, Alexander F.; de With, P.H.N.

In: Journal of Medical Imaging, Vol. 6, No. 1, 015001, 14.01.2019.

Research output: Contribution to journalArticleAcademicpeer-review

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