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Comparison of a Hybrid Mixture Model and a CNN for the Segmentation of Myocardial Pathologies in Delayed Enhancement MRI

 
: Huellebrand, M.; Ivantsits, M.; Zhang, H.; Kohlmann, P.; Kuhnigk, J.-M.; Kuehne, T.; Schönberg, S.; Hennemuth, A.

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Puyol Anton, E.:
Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges : 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers
Cham: Springer Nature, 2021 (Lecture Notes in Computer Science 12592)
ISBN: 978-3-030-68106-7 (Print)
ISBN: 978-3-030-68107-4 (Online)
ISBN: 978-3-030-68108-1
pp.319-327
International Workshop on Statistical Atlases and Computational Modelling of the Heart (STACOM) <11, 2020, Online>
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) <23, 2020, Online>
English
Conference Paper
Fraunhofer MEVIS ()

Abstract
DE-MRI provides a reliable and accurate imaging technique for the assessment of pathological alterations in myocardial tissue. The clinically applied thresholding techniques enable the assessment of the amount of diseased tissue. To also assess distribution patterns, transmurality and micro-vascular obstruction, more accurate segmentation methods are needed. We compare a hybrid CNN and mixture model approach with a two single-stage U-Net segmentation: one based on the EMIDEC challenge data set, one with additional training data, and could achieve DICE coefficients of 84.8 %, 84.08 %, and 82.95 %, respectively. We hope to further improve the promising results through an extension of the training set.

: http://publica.fraunhofer.de/documents/N-637772.html