Abstract
Cardiovascular diseases are the top cause of death worldwide. Commonly, physicians screen suspected pathological patients with histological examinations and blood tests. Since these clinical parameters are frequently ambiguous, they are routinely extended by delayed-enhancement magnetic resonance imaging of the myocardium.
We propose a method combining deep learning and classical machine learning to differentiate between pathological and normal cases. A convolutional neural network infers a segmentation of the left myocardium from a magnetic resonance image as a preliminary step. This segmentation is employed to determine radiomics-based features describing the morphology and texture of the myocardium. Subsequently, a multilayer perceptron deduces pathological cases from these radiomics features and clinical observations. The presented method demonstrates an accuracy of 0.96 and an F2-score of 0.98 on a nested cross-validation.
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Notes
- 1.
A parameter based on a physical assessment, quantifying the risk of mortality.
- 2.
A protein released in large quantities in the event of damage to the heart muscle cells.
- 3.
A protein released in large quantities when the heart needs to work harder.
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Ivantsits, M., Huellebrand, M., Kelle, S., Schönberg, S.O., Kuehne, T., Hennemuth, A. (2021). Deep-Learning-Based Myocardial Pathology Detection. In: Puyol Anton, E., et al. Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges. STACOM 2020. Lecture Notes in Computer Science(), vol 12592. Springer, Cham. https://doi.org/10.1007/978-3-030-68107-4_38
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DOI: https://doi.org/10.1007/978-3-030-68107-4_38
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