Automated Segmentation and Geometrical Modeling of the Tricuspid Aortic Valve in 3D Echocardiographic Images
Open accessAbstract
The aortic valve has been described with variable anatomical definitions, and the consistency of 2D manual measurement of valve dimensions in medical image data has been questionable. Given the importance of image-based morphological assessment in the diagnosis and surgical treatment of aortic valve disease, there is considerable need to develop a standardized framework for 3D valve segmentation and shape representation. Towards this goal, this work integrates template-based medial modeling and multi-atlas label fusion techniques to automatically delineate and quantitatively describe aortic leaflet geometry in 3D echocardiographic (3DE) images, a challenging task that has been explored only to a limited extent. The method makes use of expert knowledge of aortic leaflet image appearance, generates segmentations with consistent topology, and establishes a shape-based coordinate system on the aortic leaflets that enables standardized automated measurements. In this study, the algorithm is evaluated on 11 3DE images of normal human aortic leaflets acquired at mid systole. The clinical relevance of the method is its ability to capture leaflet geometry in 3DE image data with minimal user interaction while producing consistent measurements of 3D aortic leaflet geometry.
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Multi-atlas segmentation has been applied successfully to many challenging segmentation problems such as identifying hippocampal subfields in MRI, the spinal cord, and the mitral valve in preoperative 3D echocardiography.
“This approach has been applied successfully to many challenging segmentation problems 15 such as identifying hippocampal subfields in magnetic resonance images (MRI) of the brain, 17,18 the spinal cord, 19 and the mitral valve in preoperative 3D echocardiography. 20”
From Minimally interactive placenta segmentation from three-dimensional ultrasound images (2020) · cites this paper as [20]
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Minimally interactive placenta segmentation from three-dimensional ultrasound images
Oguz, I., Yushkevich, N., Pouch, A. M., Oguz, B. U., Wang, J., Parameshwaran, S., Gee, J. C., Yushkevich, P. A., & Schwartz, N. (2020) · Journal of Medical Imaging · doi:10.1117/1.jmi.7.1.014004 · Cited by 13 · see citing works
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- Authors
- Pouch, A. M., Wang, H., Takabe, M., Jackson, B. M., Sehgal, C. M., Gorman, J. H., Gorman, R. C., & Yushkevich, P. A.
- Affiliations
- University of Pennsylvania (US)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Volume
- 16
- Issue
- Pt 1
- Pages
- 485-492
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- PMC
- PMC3918680
- OpenAlex
- W93455141
- Cited by
- 25
- References
- 17
- Citations by year
- 2023: 1 · 2022: 3 · 2021: 4 · 2020: 3 · 2019: 1 · 2017: 2 · 2016: 7 · 2015: 2 · 2014: 2
- FWCI
- 2.34
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- American Heart Association
- National Institutes of Health (HL103723, EB014346)
- National Institute on Aging (AG037376, R01AG037376)
- National Heart, Lung, and Blood Institute (HL 103723, HL-073021, R01HL063954, R01HL073021, HL063954, R01HL103723, F32HL119010)
- National Institute of Biomedical Imaging and Bioengineering (R01EB014346, EB014346)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac Valve Diseases and Treatments
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Keywords
- tricuspid aortic valve, three-dimensional echocardiography, automatic segmentation, multi-atlas label fusion, template-based segmentation, aortic valve disease
- MeSH terms
- Algorithms, Aortic Valve, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Sensitivity and Specificity, Subtraction Technique, Tricuspid Valve, Reproducibility of Results, Echocardiography, Three-Dimensional, Imaging, Three-Dimensional
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