Abstract
Understanding myocardial remodelling, and developing tools for its accurate quantification, is fundamental for improving the diagnosis and treatment of myocardial infarction patients. Conventional clinical metrics, such as blood pool volume or ejection fraction, are not always distinctive. Here we describe a method for the classification of myocardial infarction from 3D diastolic and systolic left ventricle shapes, represented by point sets. Classification features included global geometric, shape and thickness descriptors, and a random forest was used for classification. Results from cross validation show an accuracy of 92.5 % (leave-one-out) and 91.5 % (5-fold), improving the 87 % obtained with ejection fraction thresholds. These results suggest that refined remodelling metrics provide information beyond standard clinical descriptors.
The original version of this chapter was revised.
An erratum to this chapter can be found at 10.1007/978-3-319-28712-6_24.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-28712-6_24
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This work was supported by funding from the Medical Research Council (MRC) and Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/L016052/1].
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Allen, J., Zacur, E., Dall’Armellina, E., Lamata, P., Grau, V. (2016). Myocardial Infarction Detection from Left Ventricular Shapes Using a Random Forest. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2015. Lecture Notes in Computer Science(), vol 9534. Springer, Cham. https://doi.org/10.1007/978-3-319-28712-6_20
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