Yu Renping, Deng Minghui, Yap Pew-Thian, Wei Zhihui, Wang Li, Shen Dinggang
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China; Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA.
College of Electrical and Information, Northeast Agricultural University, Harbin, China.
Mach Learn Med Imaging. 2016 Oct;10019:213-220. doi: 10.1007/978-3-319-47157-0_26. Epub 2016 Oct 1.
Brain magnetic resonance image segmentation is one of the most important tasks in medical image analysis and has considerable importance to the effective use of medical imagery in clinical and surgical setting. In particular, the tissue segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain measurement and disease diagnosis. A variety of studies have shown that the learning-based techniques are efficient and effective in brain tissue segmentation. However, the learning-based segmentation methods depend largely on the availability of good training labels. The commonly used 3T magnetic resonance (MR) images have insufficient image quality and often exhibit poor intensity contrast between WM, GM, and CSF, therefore not able to provide good training labels for learning-based methods. The advances in ultra-high field 7T imaging make it possible to acquire images with an increasingly high level of quality. In this study, we propose an algorithm based on random forest for segmenting 3T MR images by introducing the segmentation information from their corresponding 7T MR images (through semi-automatic labeling). Furthermore, our algorithm iteratively refines the probability maps of WM, GM, and CSF via a cascade of random forest classifiers to improve the tissue segmentation. Experimental results on 10 subjects with both 3T and 7T MR images in a leave-one-out validation, show that the proposed algorithm performs much better than the state-of-the-art segmentation methods.
脑磁共振图像分割是医学图像分析中最重要的任务之一，对于在临床和手术环境中有效利用医学图像具有相当重要的意义。特别是，白质（WM）、灰质（GM）和脑脊液（CSF）的组织分割对于脑部测量和疾病诊断至关重要。各种研究表明，基于学习的技术在脑组织分割中是高效且有效的。然而，基于学习的分割方法在很大程度上依赖于良好训练标签的可用性。常用的3T磁共振（MR）图像质量不足，并且WM、GM和CSF之间的强度对比度通常较差，因此无法为基于学习的方法提供良好的训练标签。超高场7T成像技术的进步使得获取质量越来越高的图像成为可能。在本研究中，我们提出了一种基于随机森林的算法，通过引入来自其相应7T MR图像的分割信息（通过半自动标记）来分割3T MR图像。此外，我们的算法通过一系列随机森林分类器迭代地细化WM、GM和CSF的概率图，以改善组织分割。在10名同时拥有3T和7T MR图像的受试者上进行留一法验证的实验结果表明，所提出的算法比当前最先进的分割方法表现要好得多。
