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. 2018 Dec;11(12):1917-1918.
doi: 10.1016/j.jcmg.2018.04.030. Epub 2018 Aug 15.

Automated Cardiac MR Scar Quantification in Hypertrophic Cardiomyopathy Using Deep Convolutional Neural Networks

Ahmed S Fahmy, Johannes Rausch, Ulf Neisius, Raymond H Chan, Martin S Maron, Evan Appelbaum, Bjoern Menze, Reza Nezafat

Automated Cardiac MR Scar Quantification in Hypertrophic Cardiomyopathy Using Deep Convolutional Neural Networks

Ahmed S Fahmy et al. JACC Cardiovasc Imaging. 2018 Dec.
No abstract available

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Figures

Figure 1.
Figure 1.. Automatic versus manual scar segmentation.
(A) Scar and myocardium segmentation results for slices from two patients. Contours resulting from manual and automatic segmentations for the epicardium (blue), endocardium (red), and scar (yellow) boundaries are overlaid on late gadolinium enhancement images. (B) Scatter plot of the automatic versus manual segmentation of scar volume.
See this image and copyright information in PMC

References

    1. Chan RH, Maron BJ, Olivotto I, et al. Prognostic Value of Quantitative ContrastEnhanced Cardiovascular Magnetic Resonance for the Evaluation of Sudden Death Risk in Patients With Hypertrophic Cardiomyopathy. Circulation 2014;130:484–95. - PubMed
    1. Engblom H, Tufvesson J, Jablonowski R, et al. A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data. J Cardiovasc Magn Reson 2016;18:27. - PMC - PubMed
    1. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015: 18th International Conference, Munich, Germany, Proceedings, Part III Cham: Springer International Publishing; 2015. p. 234–41.

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