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Received：11 June 2020，
Revised：2020-7-3，
Accepted：10 July 2020，
Published：16 October 2020
移动端阅览
磁共振（magnetic resonance，MR）成像作为一种安全非侵入式的成像技术，可以提供高分辨率且具有不同对比度的大脑图像，被越来越多地应用于婴儿大脑研究中。将婴儿脑MR图像准确地分割为灰质、白质和脑脊液，是研究早期大脑发育模式不可或缺的基础处理环节。由于在等强度阶段（6~9月龄）婴儿脑MR图像中，灰质和白质信号强度基本一致，组织对比度极低，导致此阶段的脑组织分割非常具有挑战性。基于深度学习的等强度婴儿脑MR图像分割方法，由于其卓越的性能受到研究人员的广泛关注，但目前尚未有文献对该领域的方法进行系统总结和分析。因此本文对目前基于深度学习的等强度婴儿脑MR图像分割方法进行了系统总结，从基本思想、网络架构、性能及优缺点4个方面进行了介绍。并针对其中的典型算法在iSeg-2017数据集上的分割结果进行了对比分析，最后对等强度婴儿脑MR图像分割中存在的问题及未来研究方向进行展望。本文通过对目前基于深度学习的等强度婴儿脑MR图像分割方法进行总结，可以看出深度学习方法已经在等强度期婴儿脑分割中展现出巨大优势，相比传统方法在分割精度和效率上均有较大提升，将进一步促进人类人脑早期发育研究。
Emerging interest has been shown in the study of infant brain development by using magnetic resonance (MR) imaging because it provides a safe and noninvasive way of examining cross-sectional views of the brain in multiple contrasts. Quantitative analysis of brain MR images is a conventional routine for many neurological diseases and conditions
which relies on the accurate segmentation of structures of interest. Accurate segmentation of infant brain MR images into white matter (WM)
gray matter (GM)
and cerebrospinal fluid is of great importance in studying and measuring normal and abnormal early brain development. However
in the isointense phase (6-9 months of age)
WM and GM exhibit similar levels of intensity in T1-weighted and T2-weighted MR images due to the inherent myelination and maturation
posing significant challenges for automated segmentation. Compared with traditional methods
deep learning-based methods have greatly improved the accuracy and efficiency of isointense infant brain segmentation. Thus
deep learning-based segmentation methods have been increasingly used by researchers due to their excellent performance. Nevertheless
no literature has systematically summarized and analyzed the methods in this field. The current study aims to review existing deep learning-based approaches for isointense infant brain MR image segmentation. With the extensive research in the literature
we systematically summarized the current deep learning-based methods for isointense infant brain MR image segmentation. We first introduced an authoritative isointense infant brain segmentation dataset
which was used in the iSeg-2017 challenge
hosted as a part of the Medical Image Computing and Computer Assisted Intervention Society Conference 2017. Afterward
several evaluation metrics
including Dice coefficient
95th-percentile Hausdorff distance
and average symmetric surface distance
were briefly described. We classified the existing deep learning-based methods for isointense infant brain MR image segmentation into two categories: 2D and fully convolutional neural network-based methods. Fully convolutional neural network-based methods can be further divided into two subcategories: 2D and 3D network-based approaches. On the basis of the two categories
we comprehensively described and analyzed the basic ideas
network architecture
improvement schemes
and segmentation performance of each method. In addition
we compared the performance of some existing deep learning-based methods and summarized the analysis results of typical methods on the iSeg-2017 dataset. Lastly
three possible research directions of isointense infant brain MR image segmentation methods based on deep learning were discussed. We drew three main conclusions by reviewing the main work in this field
thereby providing a good overview of existing deep learning methods for isointense infant brain MR image segmentation. First
using multimodality data is beneficial for the network to obtain rich feature information
which can improve the segmentation accuracy. Second
compared with 2D fully convolutional network-based methods
3D-based methods for MR image brain segmentation can integrate richer spatial context information
resulting in higher accuracy and efficiency. Third
adopting complicated network architecture and dense network connection could make the network achieve superior accuracy performance. Three possible future directions
namely
embedding powerful feature representation modules (e.g.
attention mechanism)
adding prior knowledge of the infant brain to the network model (e.g.
the cortical thickness of infant brain is within a certain range)
and constructing a semisupervised or weakly supervised network model trained with a small amount of labeled data
were recommended. Precise segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain development. Deep learning has shown great advantages in isointense infant brain segmentation
and its accuracy and efficiency have been greatly improved compared with traditional methods. With the development of deep learning
it will bring further improvement in the research field of isointense infant brain segmentation and promote the research of early human brain development.
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