Abstract
Automated myocardial segmentation from late gadolinium enhancement magnetic resonance images (LGE-MRI) is a critical step in the diagnosis of cardiac pathologies such as ischemia and myocardial infarction. This paper proposes a deep learning framework for improved myocardial diseases segmentation. In the first step, we build an encoder-decoder segmentation network that generates myocardium and cavity segmentations from the whole volume, followed by a 3D U-Net based on Shape prior to identifying myocardial infarction and myocardium ventricular obstruction (MVO) segmentations from the encoder-decoder prediction. The proposed network achieves good segmentation performance, as computed by average Dice ratio overall predicted substructures, respectively: ’Myocardium’: 96.29%, ’Infarctus’: 76.56%, ’MVO’: 93.12% on our validation EMIDEC dataset consisting of LGE-MRI volumes of 16 patients extracted from the training data.
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Brahim, K., Qayyum, A., Lalande, A., Boucher, A., Sakly, A., Meriaudeau, F. (2021). Efficient 3D Deep Learning for Myocardial Diseases Segmentation. 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_37
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