Automatic quantification of the LV function and mass: A deep learning approach for cardiovascular MRI
Automatic quantification of the LV function and mass: A deep learning approach for cardiovascular MRI
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DOI：
10.1016/j.cmpb.2018.12.002
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发表时间：
2019-02-01
影响因子：
6.1
通讯作者：
Mato, German
中科院分区：
文献类型：
--
作者：
Curiale, Ariel H.;Colavecchia, Flavio D.;Mato, German
目的：本文提出了一种新的方法，自动左心室（LV）量化使用卷积神经网络（CNN）。方法：一般框架由一个CNN检测LV，另一个组织分类。此外，还提出了三种新的深度学习架构用于LV量化。这些新的CNN将稀疏性和深度可分离卷积的思想引入到U-网络架构中，以及一种逐层的残差学习策略。为此，我们扩展了经典的U-网络架构，并使用广义Jaccard距离作为优化目标functions.Results：CNN的训练和评估与140例患者从两个公共心血管磁共振数据集（Sunnybrook和心脏Atlas项目）通过使用5倍交叉验证策略。我们的结果表明，心肌分割的准确性是合适的（类似于0.9 Dice's系数），并且与最相关的生理指标有很强的相关性：舒张末期和收缩末期容积为0.99，左心肌质量为0.97，射血分数为0.95，每搏输出量和心输出量为0.93。我们的模拟和临床评估结果证明了所提出的CNN估计不同结构和功能特征（例如LV质量和EF）的能力和优点，这些特征通常用于诊断和治疗不同的病理。本文提出了一种基于深度学习的自动LV量化的新方法，其中误差与手动轮廓绘制的操作员间和操作员内范围相当。(C)2018爱思唯尔B. V.保留所有权利。
Objective: This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN).Methods: The general framework consists of one CNN for detecting the LV, and another for tissue classification. Also, three new deep learning architectures were proposed for LV quantification. These new CNNs introduce the ideas of sparsity and depthwise separable convolution into the U-net architecture, as well as, a residual learning strategy level-to-level. To this end, we extend the classical U-net architecture and use the generalized Jaccard distance as optimization objective function.Results: The CNNs were trained and evaluated with 140 patients from two public cardiovascular magnetic resonance datasets (Sunnybrook and Cardiac Atlas Project) by using a 5-fold cross-validation strategy. Our results demonstrate a suitable accuracy for myocardial segmentation (similar to 0.9 Dice's coefficient), and a strong correlation with the most relevant physiological measures: 0.99 for end-diastolic and end-systolic volume, 0.97 for the left myocardial mass, 0.95 for the ejection fraction and 0.93 for the stroke volume and cardiac output.Conclusion: Our simulation and clinical evaluation results demonstrate the capability and merits of the proposed CNN to estimate different structural and functional features such as LV mass and EF which are commonly used for both diagnosis and treatment of different pathologies.Significance: This paper suggests a new approach for automatic LV quantification based on deep learning where errors are comparable to the inter- and intra-operator ranges for manual contouring. (C) 2018 Elsevier B.V. All rights reserved.