0000000000821582
AUTHOR
Khawla Brahim
showing 4 related works from this author
A 3D deep learning approach based on Shape Prior for automatic segmentation of myocardial diseases
2020
Accurate three-dimensional (3D) cardiac segmentation from late gadolinium enhancement (LGE)-MRI plays a critical role in designing a structure of reference for diagnosing many cardiac pathologies such as ischemia, myocarditis and myocardial infarction. This segmentation is however still a non-trivial task, due to the motion artifacts during acquisition, and heterogeneous intensity distributions. In this study, we develop a fully 3D automated model based on deep neural networks (DNN) for LGE-MRI myocardial pathologies (scar and No-reflow tissues) segmentation in a new expert annotated dataset. Considering that damaged tissue constitutes a small area of the whole LGE-MRI, we concentrated on m…
Deep learning architectures for automatic detection of viable myocardiac segments
2021
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. …
A deep learning approach for the segmentation of myocardial diseases
2021
Cardiac left ventricular (LV) segmentation is a paramount essential step for both diagnosis and treatment of cardiac pathologies such as ischemia, myocardial infarction, arrhythmia and myocarditis. However, this segmentation is challenging due to high variability across patients and the potential lack of contrast between structures. In this work, we propose and evaluate a (2.5D) SegU-Net model based on the fusion of two deep learning segmentation techniques (U-Net and Seg-Net) for automated LGE-MRI (Late gadolinium enhanced magnetic resonance imaging) myocardial disease (infarct core and no-reflow region) quantification in a new multifield expert annotated dataset. Given that the scar tissu…
Efficient 3D Deep Learning for Myocardial Diseases Segmentation
2021
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 avera…