School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States.
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China; Shenzhen Huazhong University of Science and Technology Research Institute, China.
Med Image Anal. 2020 Feb;60:101602. doi: 10.1016/j.media.2019.101602. Epub 2019 Nov 8.
It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance. In this study, we propose subsequent boundary distance regression and pixel classification networks to segment the kidneys automatically. Particularly, we first use deep neural networks pre-trained for classification of natural images to extract high-level image features from US images. These features are used as input to learn kidney boundary distance maps using a boundary distance regression network and the predicted boundary distance maps are classified as kidney pixels or non-kidney pixels using a pixelwise classification network in an end-to-end learning fashion. We also adopted a data-augmentation method based on kidney shape registration to generate enriched training data from a small number of US images with manually segmented kidney labels. Experimental results have demonstrated that our method could automatically segment the kidney with promising performance, significantly better than deep learning-based pixel classification networks.
由于肾脏的形状和图像强度分布各不相同，因此在临床超声（US）图像中自动分割肾脏仍然具有挑战性，尽管半自动方法已经取得了有前景的性能。在这项研究中，我们提出了后续边界距离回归和像素分类网络来自动分割肾脏。特别地，我们首先使用针对自然图像分类预训练的深度神经网络从 US 图像中提取高级图像特征。这些特征被用作输入，使用边界距离回归网络学习肾脏边界距离图，并使用端到端学习方式的像素分类网络将预测的边界距离图分类为肾脏像素或非肾脏像素。我们还采用了基于肾脏形状注册的扩充数据方法，从少数带有手动分割肾脏标签的 US 图像中生成丰富的训练数据。实验结果表明，我们的方法可以自动分割肾脏，性能优异，明显优于基于深度学习的像素分类网络。
