A 3D Network Based Shape Prior for Automatic Myocardial Disease Segmentation in Delayed-Enhancement MRI
A 3D Network Based Shape Prior for Automatic Myocardial Disease Segmentation in Delayed-Enhancement MRI
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DOI：
10.1016/j.irbm.2021.02.005
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发表时间：
2021-11-16
期刊：
影响因子：
4.8
通讯作者：
Meriaudeau, F.
中科院分区：
文献类型：
--
作者：
Brahim, K.;Qayyum, A.;Meriaudeau, F.
目的：在这项工作中，提出了一种新的基于晚期钆增强(LGE)-MRI的相关心肌梗死分割的深度学习模型。此外，我们的新分割方法旨在准确检测微血管阻塞区域。材料与方法：首先对解剖结构进行分割，即左室腔和心肌进行初步分割。然后，采用一种基于形状先验的框架，将3D U-Net架构与3D Autoencoder分割框架相融合，约束病理组织的分割过程。结果：与人类水平的性能相比，所提出的网络在由从训练EMIDEC数据集中选择的16个DE-MRI体积组成的验证集上，心肌的平均Dice得分为0.9507，疤痕的平均Dice得分为0.7656，MVO的平均Dice得分为0.8377，达到了出色的心肌分割效果。结论：我们的方法在三个带注释的数据集上进行了广泛的验证，并与现有最先进的深度学习模型进行了全面的比较，包括健康和患病检查，使该建议成为增强MI诊断的可靠工具。(c) 2021 agbm。Elsevier Masson SAS出版。版权所有。
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. (C) 2021 AGBM. Published by Elsevier Masson SAS. All rights reserved.