A 3D Network Based Shape Prior for Automatic Myocardial Disease Segmentation in Delayed-Enhancement MRI - 27/02/21
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Graphical abstract |
Highlights |
• | A pre-trained 3D Autoencoder is used to reconstruct an identity segmentation result. |
• | Optimum setting parameters are useful for segmentation and computational performance. |
• | Propose volume patch extraction from Delayed Enhancement Magnetic Resonance Imaging. |
• | A fusion of 3D Autoencoder with 3D U-Net proves better myocardial segmentation. |
• | An anatomical network is used to segment the myocardium and left ventricular cavity. |
Abstract |
Objectives: In this work, a new deep learning model for relevant myocardial infarction segmentation from Late Gadolinium Enhancement (LGE)-MRI is proposed. Moreover, our novel segmentation method aims to detect microvascular-obstructed regions accurately. Material and methods: We first segment the anatomical structures, i.e., the left ventricular cavity and the myocardium, to achieve a preliminary segmentation. Then, a shape prior based framework that fuses the 3D U-Net architecture with 3D Autoencoder segmentation framework to constrain the segmentation process of pathological tissues is applied. Results: The proposed network reached outstanding myocardial segmentation compared with the human-level performance with the average Dice score of ‘0.9507’ for myocardium, ‘0.7656’ for scar, and ‘0.8377’ for MVO on the validation set consisting of 16 DE-MRI volumes selected from the training EMIDEC dataset. Conclusion: It is concluded that our approach's extensive validation and comprehensive comparison against existing state-of-the-art deep learning models on three annotated datasets, including healthy and diseased exams, make this proposal a reliable tool to enhance MI diagnosis.
Le texte complet de cet article est disponible en PDF.Keywords : Myocardial infarction segmentation, LGE-MRI, Microvascular-obstructed regions
Plan
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