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首页> 外文期刊>IEEE Transactions on Medical Imaging >Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning
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Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning

机译：通过联合表示和回归学习的直接多型心脏指数估计

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摘要

Cardiac indices estimation is of great importance during identification and diagnosis of cardiac disease in clinical routine. However, estimation of multitype cardiac indices with consistently reliable and high accuracy is still a great challenge due to the high variability of cardiac structures and the complexity of temporal dynamics in cardiac MR sequences. While efforts have been devoted into cardiac volumes estimation through feature engineering followed by a independent regression model, these methods suffer from the vulnerable feature representation and incompatible regression model. In this paper, we propose a semi-automated method for multitype cardiac indices estimation. After the manual labeling of two landmarks for ROI cropping, an integrated deep neural network Indices-Net is designed to jointly learn the representation and regression models. It comprises two tightly-coupled networks, such as a deep convolution autoencoder for cardiac image representation, and a multiple output convolution neural network for indices regression. Joint learning of the two networks effectively enhances the expressiveness of image representation with respect to cardiac indices, and the compatibility between image representation and indices regression, thus leading to accurate and reliable estimations for all the cardiac indices. When applied with five-fold cross validation on MR images of 145 subjects, Indices-Net achieves consistently low estimation error for LV wall thicknesses (1.44 ± 0.71 mm) and areas of cavity and myocardium (204 ± 133 mm). It outperforms, with significant error reductions, segmentation method (55.1% and 17.4%), and two-phase direct volume-only methods (12.7% and 14.6%) for wall thicknesses and areas, respectively. These advantages endow the proposed method a great potential in clinical cardiac function assessment.
机译：在临床常规的心脏疾病的识别和诊断过程中，心脏指数的估计非常重要。但是，由于心脏结构的高度可变性以及心脏MR序列中时间动态的复杂性，以始终如一的可靠和高精度估算多种类型的心脏指数仍然是一个巨大的挑战。尽管人们一直致力于通过特征工程以及随后的独立回归模型进行心脏容积估计，但这些方法仍存在脆弱的特征表示和不兼容的回归模型。在本文中，我们提出了一种用于多类型心脏指数估计的半自动化方法。在为ROI裁剪手动标记两个界标之后，设计了一个集成的深度神经网络Indices-Net，以共同学习表示和回归模型。它包括两个紧密耦合的网络，例如用于心脏图像表示的深度卷积自动编码器和用于索引回归的多输出卷积神经网络。这两个网络的联合学习有效地增强了图像代表相对于心脏指数的表达能力，以及图像代表和指数回归之间的兼容性，从而导致对所有心脏指数的准确而可靠的估计。当对145个受试者的MR图像进行五重交叉验证时，Indices-Net对LV壁厚（1.44±0.71 mm）以及腔和心肌面积（204±133 mm）始终保持较低的估计误差。对于壁厚和面积，该方法的性能要好得多，并显着减少了误差，采用分割方法（55.1％和17.4％）和仅两相直接体积法（12.7％和14.6％）。这些优点使所提出的方法在临床心功能评估中具有很大的潜力。

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