New automated Markov-Gibbs random field based framework for myocardial wall viability quantification on agent enhanced cardiac magnetic resonance images.
A novel automated framework for detecting and quantifying viability from agent enhanced cardiac magnetic resonance images is proposed. The framework identifies the pathological tissues based on a joint Markov-Gibbs random field (MGRF) model that accounts for the 1st-order visual appearance of the my...
| Publicado en: | International Journal of Cardiovascular Imaging Vol. 28; no. 7; pp. 1683 - 1699 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Oct2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104375183&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104375183 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15695794 1HHY jtl: International Journal of Cardiovascular Imaging issn: 15695794 maglogo: N pubinfo: dt: Oct2012 vid: 28 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104375183 NLM22160668 2011718427 10.1007/s10554-011-9991-2 NLM22160668 104375183 ppf: 1683 ppct: 16 formats: tig: atl: New automated Markov-Gibbs random field based framework for myocardial wall viability quantification on agent enhanced cardiac magnetic resonance images. aug: au: Elnakib A Beache GM Gimel'farb G El-Baz A Elnakib, Ahmed Beache, Garth M Gimel'farb, Georgy El-Baz, Ayman affil: BioImaging Laboratory, Department of Bioengineering, University of Louisville, Room 423 Lutz Hall, Louisville, KY 40292, USA sug: subj: Contrast Media Diagnostic Use Heart Diseases Pathology Image Interpretation, Computer Assisted Magnetic Resonance Imaging Equipment and Supplies Models, Biological Myocardium Pathology Algorithms Automation, Laboratory Human Observer Bias Phantoms, Imaging Predictive Value of Tests Reproducibility of Results Biological Phenomena ab: A novel automated framework for detecting and quantifying viability from agent enhanced cardiac magnetic resonance images is proposed. The framework identifies the pathological tissues based on a joint Markov-Gibbs random field (MGRF) model that accounts for the 1st-order visual appearance of the myocardial wall (in terms of the pixel-wise intensities) and the 2nd-order spatial interactions between pixels. The pathological tissue is quantified based on two metrics: the percentage area in each segment with respect to the total area of the segment, and the trans-wall extent of the pathological tissue. This transmural extent is estimated using point-to-point correspondences based on a Laplace partial differential equation. Transmural extent was validated using a simulated phantom. We tested the proposed framework on 14 datasets (168 images) and validated against manual expert delineation of the pathological tissue by two observers. Mean Dice similarity coefficients (DSC) of 0.90 and 0.88 were obtained for the observers, approaching the ideal value, 1. The Bland-Altman statistic of infarct volumes estimated by manual versus the MGRF estimation revealed little bias difference, and most values fell within the 95% confidence interval, suggesting very good agreement. Using the DSC measure we documented statistically significant superior segmentation performance for our MGRF method versus established intensity-based methods (greater DSC, and smaller standard deviation). Our Laplace method showed good operating characteristics across the full range of extent of transmural infarct, outperforming conventional methods. Phantom validation and experiments on patient data confirmed the robustness and accuracy of the proposed framework. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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