6533b85efe1ef96bd12bf8c4

RESEARCH PRODUCT

Deep learning architectures for automatic detection of viable myocardiac segments

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Thesis abstract: Deep learning architectures for automatic detection of viable myocardiac segmentsAccurate myocardial segmentation in LGE-MRI is an important purpose for diagnosis assistance of infarcted patients. Nevertheless, manual delineation of target volumes is time-consuming and depends on intra- and inter-observer variability. This thesis aims at developing efficient deep learning-based methods for automatically segmenting myocardial tissues (healthy myocardium, myocardial infarction, and microvascular obstruction) on LGE-MRI. In this regard, we first proposed a 2.5D SegU-Net model based on a fusion framework (U-Net and SegNet) to learn different feature representations adaptively. Then, we extended to new 3D architectures to benefit from additional depth cues. In a second step, we proposed to segment the anatomical structures using inception residual block and convolutional block attention module and diseased regions using 3D Auto-encoder to perfect myocardial shape. To this end, a prior shape penalty term is added to 3D U-Net architecture. Finally, we proposed first segment the left ventricular cavity and the myocardium based on the no-new-U-Net and second use a priori inclusion and classification networks to maintain the topological constraints of pathological tissues within the pre-segmented myocardium. We have introduced a post-processing decision phase to reduce the uncertainty of the model. The state-of-the-art performance of the proposed methods is validated on the EMIDEC dataset, comprising 100 training images and 50 test images from healthy and infarcted patients. Comprehensive empirical evaluations show that all of our algorithms have promising results.