The DOI matches its Crossref record; 52 of the 53 references it cites were found in a registry or an index.
A shape-space-based approach to tracking myocardial borders and quantifying regional left-ventricular function applied in echocardiography
Abstract
This paper presents a new semi-automatic method for quantifying regional heart function from two-dimensional echocardiography. In the approach, we first track the endocardial and epicardial boundaries using a new variant of the dynamic snake approach. The tracked borders are then decomposed into clinically meaningful regional parameters, using a novel interpretational shape-space motivated by the 16-segment model used in clinical practice for qualitative assessment of heart function. We show how a quantitative and automatic scoring scheme for the endocardial excursion and myocardial thickening can be derived from this. Results illustrating our approach on apical long-axis two-chamber-view data from a patient with a myocardial infarct in the apical anterior/inferior region of the heart are presented. In a case study (five patients, nine data sets) the performance of the tracking and interpretation techniques are compared with manual delineations of borders using a number of quantitative measures of regional comparison.
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“To address these issues, various conventional machine learning methods such as active contours [127] and deformable templates [128] have been widely used to successfully segment the LV, under the condition of using prior knowledge about the LV shape and appearance.”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [128]
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Flagged first, then by citations
1 of the 53 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Behrenbruch, Adaptive multiresolution detection of the epicardial wall in 2-D cardiac ultrasound, 89
- Meyer, Progress in wavelet analysis and applications, 77
- Young, Motion tags: direct 3-D tracking of heart wall motion from tagged MR images, 92
Cited by
Sorted by citations
Ultrasound image segmentation: a survey
Noble, J. A., & Boukerroui, D. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.877092 · Cited by 1,157
“A variant of this is to use a Kalman filter to blend a spatio-temporal model with feature measurements as in [45], [46].”
MethodInfluentialDeep Learning in Medical Ultrasound Analysis: A Review
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798
“To address these issues, various conventional machine learning methods such as active contours [127] and deformable templates [128] have been widely used to successfully segment the LV, under the condition of using prior knowledge about the LV shape and appearance.”
MethodSpatio-temporal nonrigid registration for ultrasound cardiac motion estimation
Ledesma‐Carbayo, M. J., Kybic, J., Desco, M., Santos, A., Sühling, M., Hunziker, P., & Unser, M. A. (2005) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2005.852050 · Cited by 290
“the intrinsic complexity of the data by introducing a priori knowledge by modeling a statistical representation of the possible motions and shapes [24], [25].”
BackgroundSnakes based segmentation of the common carotid artery intima media
Loizou, C. P., Pattichis, C. S., Pantziaris, M., Tyllis, T., & Nicolaides, A. N. (2007) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-006-0140-3 · Cited by 227
“The Bland–Altman test was also used in [10] to compare the results of manual and automated detection in myocardial borders, and the segmented border values between manual and automated detection in brachial ultrasound images [28].”
MethodAn electromechanical model of the heart for image analysis and simulation
Maxime Sermesant, B., Delingette, H., & Ayache, N. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.872746 · Cited by 214
“At the same time, deformable templates evolved toward sophisticated approaches, for instance combined with Bayesian classification and Markov random fields [26], or coupling shape-space and Kalman-filter-based tracking [ 27 ].”
Method
Metadata
- Authors
- Jacob, G., Noble, J. A., Behrenbruch, C., Kelion, A. D., & Banning, A. P.
- Affiliations
- University of Oxford (GB)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2002-03
- Volume
- 21
- Issue
- 3
- Pages
- 226-238
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2155831290
- Cited by
- 142
- References
- 53
- Citations by year
- 2024: 2 · 2023: 2 · 2022: 4 · 2021: 1 · 2020: 3 · 2019: 6 · 2018: 1 · 2017: 1 · 2016: 2 · 2015: 6 · 2014: 8 · 2013: 15 · 2012: 10
- FWCI
- 8.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Function and Risk Factors
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Keywords
- shape space, regional left ventricular function, 16-segment model, myocardial wall thickening, two-dimensional echocardiography, myocardial infarction, semi-automatic segmentation
- MeSH terms
- Algorithms, Echocardiography, Elasticity, Heart, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Models, Cardiovascular, Movement, Myocardial Contraction, Myocardial Infarction, Sensitivity and Specificity, Models, Statistical, Ventricular Dysfunction, Left
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