Zhang Wenlu, Li Rongjian, Deng Houtao, Wang Li, Lin Weili, Ji Shuiwang, Shen Dinggang
Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.
Instacart, San Francisco, CA 94107, USA.
Neuroimage. 2015 Mar;108:214-24. doi: 10.1016/j.neuroimage.2014.12.061. Epub 2015 Jan 3.
The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6-8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multi-modality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement.
将婴儿脑组织图像分割为白质（WM）、灰质（GM）和脑脊液（CSF）在研究健康和疾病状态下的早期脑发育过程中起着重要作用。在等强度阶段（大约6 - 8个月大），白质和灰质在T1和T2磁共振图像中表现出相似的强度水平，这使得组织分割极具挑战性。现有的方法中只有少数是针对这个等强度阶段的组织分割设计的；然而，它们仅使用单个T1或T2图像，或者T1和T2图像的组合。在本文中，我们提出使用深度卷积神经网络（CNN），通过多模态磁共振图像对等强度阶段的脑组织进行分割。CNN是一种深度模型，其中可训练滤波器和局部邻域池化操作交替应用于原始输入图像，从而产生越来越复杂的特征层次结构。具体而言，我们将来自T1、T2和分数各向异性（FA）图像的多模态信息作为输入，然后生成分割图作为输出。多个中间层应用卷积、池化、归一化和其他操作来捕捉输入和输出之间的高度非线性映射。我们在一组手动分割的等强度阶段脑图像上，将我们的方法与常用分割方法的性能进行了比较。结果表明，我们提出的模型在婴儿脑组织分割方面显著优于先前的方法。此外，我们的结果表明多模态图像的整合导致了性能的显著提升。
