Lin Adan, Wu Junhao, Yang Xuan
College of Computer Science and Software Engineering, Shenzhen University, 518060, China.
College of Computer Science and Software Engineering, Shenzhen University, 518060, China.
Magn Reson Imaging. 2020 Feb;66:152-164. doi: 10.1016/j.mri.2019.08.004. Epub 2019 Aug 30.
Left ventricle (LV) segmentation plays an important role in the diagnosis of cardiovascular diseases. The cardiac contractile function can be quantified by measuring the segmentation results of LVs. Fully convolutional networks (FCNs) have been proven to be able to segment images. However, a large number of annotated images are required to train the network to avoid overfitting, which is a challenge for LV segmentation owing to the limited small number of available training samples. In this paper, we analyze the influence of augmenting training samples used in an FCN for LV segmentation, and propose a data augmentation approach based on shape models to train the FCN from a few samples. We show that the balanced training samples affect the performance of FCNs greatly. Experiments on four public datasets demonstrate that the FCN trained by our augmented data outperforms most existing automated segmentation methods with respect to several commonly used evaluation measures.
左心室（LV）分割在心血管疾病诊断中起着重要作用。通过测量左心室的分割结果可以量化心脏收缩功能。全卷积网络（FCN）已被证明能够对图像进行分割。然而，需要大量带注释的图像来训练网络以避免过拟合，由于可用训练样本数量有限，这对左心室分割来说是一个挑战。在本文中，我们分析了用于左心室分割的全卷积网络中增强训练样本的影响，并提出了一种基于形状模型的数据增强方法，以便从少量样本中训练全卷积网络。我们表明，平衡的训练样本对全卷积网络的性能有很大影响。在四个公共数据集上进行的实验表明，使用我们增强后的数据训练的全卷积网络在几种常用评估指标方面优于大多数现有的自动分割方法。
