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]
Citations
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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
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Items 101–142 of 142
Myocardial Motion and Strain Rate Analysis from Ultrasound Sequences
Sühling, M., Arigovindan, M., Jansen, C. H. P., Hunziker, P., & Unser, M. A. (2007) · Springer eBooks · doi:10.1007/978-3-540-69866-1_13 · Cited by 3
“approach was extended to active shape models, which were applied to echocardiograms with partial success [3].”
MethodLEARNING METHODS IN SEGMENTATION OF CARDIAC TAGGED MRI
Qian, Z. Y., Metaxas, D., & Axel, L. (2007) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2007.356945 · Cited by 3
“In [11, 12] shape-space based or contour-matching based approaches are proposed.”
BackgroundSegmentation of Medical Images, Applications in Echocardiography and Nuclear Medicine
Landgren, M. (2014) · Lund University Publications (Lund University) · Cited by 2
Deformable 3-D Modelling from Uncalibrated Video Sequences
Bue, A. D. (2013) · Cited by 2
Mitral Valve Prolapse detection using landmark extraction from echocardiography sequences
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Model-Agnostic Temporal Regularizer for Object Localization Using Motion Fields
Santiago, C., Medley, D. O., Marques, J. S., & Nascimento, J. C. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3155947 · Cited by 2
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2D local heart motion estimation using level sets and hierarchical B-splines
Mora, M., Tauber, C., & Batatia, H. (2006) · Cited by 2
Assisted Ejection Fraction in B-Mode and Contrast Echocardiography
Jolly, M. (2006) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2006.1624861 · Cited by 2
“151] by Medis Medical Imaging Systems, and Quamus [5] by Mirada Solutions.”
MethodInfluentialMorda, L. S., Lim, W. K., & Konofagou, E. E. (2006) · Ultrasonic Imaging · doi:10.1177/016173460602800303 · Cited by 2
Non-rigid object reconstruction based on multiview images: A survey
Liu, Z., Wang, H., Li, Q., & Jun, S. (2010) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2010.5648225 · Cited by 1
“However, nowadays non-rigid object reconstruction and motion analysis are areas of problem [1, 2], since there is more and more demand for 3D non-rigid object reconstruction for biological and biomedical research [3-5], and 3D-TV, the broadcast of 3D virtual television productions [6-8].”
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Transtexture based segmentation of echocardiographic images
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Automated coupled-contour and robust myocardium tracking in stress echocardiography
Karagiannis, S. E., Roelandt, J. R. T. C., Qazi, M., KRISHNAN, S., Feringa, H. H. H., Vidaković, R., Karatasakis, G., Cokkinos, D. V., & Poldermans, D. (2007) · European Journal of Echocardiography · doi:10.1016/j.euje.2007.03.002 · Cited by 1
“6) and permits accurate border motion tracking.(17,19,20)”
Cardio-Spatial Profile Extraction using Optical Flow of Echocardiographic Images
Riyadi, S., Zakaria, N., Mustafa, M. M., Hussain, A., Maskon, O., & Faizura, I. (2009) · Cited by 1
Bravo, A., Vera, M. Á., Madriz, D., Contreras-Velásquez, J., Chacón, J., Wilches-Durán, S., Graterol-Rivas, M., Riaño-Wilches, D., Rojas, J., & Bermúdez, V. (2004) · Imaging and Radiation Research · doi:10.24294/irr.v5i1.1748 · Cited by 1
“Models based on Markov random fields([34,35]), fuzzy logic([36]), neural networks([6,37]), morphological filters([38]) and active contours([39-42]) have been proposed.”
BackgroundMethodLocalization of cardiac cavities using hierarchically search in 2D grayscale ultrasound images
de Siqueira, M. L., Müller, D., & Navaux, P. O. A. (2008) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip.2008.4604408 · Cited by 1
“[4] related to the tracking of cardiac structures in ultrasound images using active mold (q)”
BackgroundQuantitative Cardiac Analysis Algorithms for Pocket-sized Ultrasound Devices
Snare, S. R. (2011) · NORA - Norwegian Open Research Archives · Cited by 1
Evaluation of the Inter-observer Cardiac Chamber Contour Extraction versus a Level Set Algorithm
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Interaktive Segmentierung von Hirninfarkten mittels Snake-Verfahren
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“Jacob et al. [ 7 ] also used a user intervention: a few contours have to be drown on the data prior to running the tracker in order to learn the nominal deformation parameters and to make placement of the initial contour.”
BackgroundMethodSzalka, Z., Soós, G., Hillier, D., Kék, L., Andrassy, G., & Rekeczky, C. (2006) · IEEE International Workshop on Cellular Neural Networks and their Applications · doi:10.1109/cnna.2006.341646
“[4] are only some examples from the rich palette of segmentation methods.”
BackgroundDikici, E., Orderud, F., Kiss, G. H., Thorstensen, A., & Torp, H. G. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.33
“This framework was later utilized for rapid tracking of LV in long-axis 2D echocardiography in [10, 11].”
MethodEcho-cardiographic segmentation: Via feature-space clustering
Gondle, V., & Sivaswamy, J. (2011) · National Conference on Communications · doi:10.1109/ncc.2011.5734776
“Some of the main problems are: i) presence of speckle noise which occurs due to back-scattering of ultrasound waves by the particles whose size is less than the wavelength of projected waves [1]; ii) blurring of the spatial information in the direction perpendicular to the ultrasound waves [2] and finally, iii) Discon…”
BackgroundA Noise Resistive Transtexture Based Delineation of Left Ventricle in Echocardiographic Images
Suphalakshmi, A., & An, P. (2013)
Segmentation of cardiac structures in 3-dimensional echocardiography
Bersvendsen, J. (2016) · NORA - Norwegian Open Research Archives · doi:10.13140/rg.2.2.25353.70245
Cardiac Chamber Contour Extraction
Roxo, D., Silva, J. S., Santos, J. B., Martins, P., Castela, E., & Martins, R. (2013) · IGI Global eBooks · doi:10.4018/978-1-4666-3667-5.ch018
Roxo, D. (2011) · Estudo Geral (Universidade de Coimbra)
Tracking of Heart Wall Motion using Unscented Kalman Filter
Hazarika, D., Mahanta, C., & Dandapat, S. (2006) · IEEE India Conference · doi:10.1109/indcon.2006.302804
Reconhecimento Automático de Padrões em
de Siqueira, M. L., & Ecocardiográficas, I. (2010)
Krishanmoorthy, S., Selvakumar, S., Priya, V. V., Mohan, S. K., & Sabithabanu, K. (2022) · Studies in fuzziness and soft computing · doi:10.1007/978-3-030-92026-5_2
Detection of Cardiac Geometry via Difference Intensity of Echocardiogram Images
Adib, M. A. H. M., Yusof, M. F., Zulkifli, A., & Hasni, N. H. M. (2012) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2012-195
“Due to feature-based methods, where they first segment the myocardial regions [7] and the motion is then recovered by aligning the segmented shapes.”
MethodA hybrid approach for tracking borders in echocardiograms
Ali, Y. A., Beheshti, S., Janabi‐Sharifi, F., Rezaii, T. Y., Cheema, A. N., & Pedrosa, J. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02250-y
“[9] introduced real-time tracking based on state estimationmethods that utilizes shape space model.”
BackgroundEchocardiography of Left Ventricular Myocardium Deformation
Karvandi, M., & Ranjbar, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-29046-6_1
Segmentation d’images échographiques pour applications spatiale
Derache, F. (2022) · theses.fr (ABES) · doi:10.70675/c55ff857zdb52z4f38zaa2bzecbc8341a101
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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