Wang Zhongrong, Xie Lipeng, Qi Jin
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Magn Reson Imaging. 2020 Feb;66:131-140. doi: 10.1016/j.mri.2019.08.021. Epub 2019 Aug 26.
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.
心脏磁共振成像（MRI）中的左心室（LV）分割是各种心血管疾病定量诊断的重要步骤。在本文中，我们提出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了分层架构的优势，并将多尺度特征整合在一起以分割左心室的心肌区域。此外，我们提出了一种动态逐像素加权策略，该策略可以根据上层的分割精度动态调整每个像素的权重，并迫使像素分类器更加关注误分类的像素。通过这种方式，我们方法的左心室分割性能可以得到很大提高，特别是对于电影磁共振图像中的心尖和基底切片。在CAP数据库上的实验表明，与其他知名的深度学习方法相比，我们的方法取得了显著改进。除此之外，我们还讨论了基于卷积神经网络的语义分割方法在左心室分割中的两个主要局限性。
