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首页> 外文期刊>Magnetic resonance imaging: An International journal of basic research and clinical applications >Dynamic pixel-wise weighting-based fully convolutional neural networks for left ventricle segmentation in short-axis MRI
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Dynamic pixel-wise weighting-based fully convolutional neural networks for left ventricle segmentation in short-axis MRI

机译：基于像素的基于加权的基于左心室分割的基于旋转的全卷积神经网络

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摘要

Left ventricle (LV) segmentation in cardiac MRI is an essential procedure for quantitative diagnosis of various cardiovascular diseases. In this paper, we present a novel fully automatic left ventricle segmentation approach based on convolutional neural networks. The proposed network fully takes advantages of the hierarchical architecture and integrate the multi-scale feature together for segmenting the myocardial region of LV. Moreover, we put forward a dynamic pixel-wise weighting strategy, which can dynamically adjust the weight of each pixel according to the segmentation accuracy of upper layer and force the pixel classifier to take more attention on the misclassified ones. By this way, the LV segmentation performance of our method can be improved a lot especially for the apical and basal slices in cine MR images. The experiments on the CAP database demonstrate that our method achieves a substantial improvement compared with other well-know deep learning methods. Beside these, we discussed two major limitations in convolutional neural networks-based semantic segmentation methods for LV segmentation.
机译：心脏MRI中的左心室（LV）分段是各种心血管疾病定量诊断的基本程序。本文介绍了一种基于卷积神经网络的新型全自动左心室分割方法。所提出的网络充分利用分层体系结构，并将多尺度特征集成在一起，以便分割LV的心肌区域。此外，我们提出了一种动态像素 - 方向加权策略，其可以根据上层的分割精度动态调节每个像素的权重，并强制像素分类器更加注意被错误分类的精度。通过这种方式，我们方法的LV分割性能可以提高很多，特别是对于Cine MR图像中的顶端和基底切片。帽数据库的实验表明，与其他知名度深入学习方法相比，我们的方法实现了大量改进。除此之外，我们讨论了LV分段的卷积神经网络的语义分段方法的两个主要限制。

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