Ismail Marwa, Soliman Ahmed, Ghazal Mohammed, Switala Andrew E, Gimel'farb Georgy, Barnes Gregory N, Khalil Ashraf, El-Baz Ayman
Bioengineering Department, University of Louisville, Louisville, KY, United States of America.
Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates.
PLoS One. 2017 Nov 14;12(11):e0187391. doi: 10.1371/journal.pone.0187391. eCollection 2017.
This paper introduces a new framework for the segmentation of different brain structures (white matter, gray matter, and cerebrospinal fluid) from 3D MR brain images at different life stages. The proposed segmentation framework is based on a shape prior built using a subset of co-aligned training images that is adapted during the segmentation process based on first- and second-order visual appearance characteristics of MR images. These characteristics are described using voxel-wise image intensities and their spatial interaction features. To more accurately model the empirical grey level distribution of the brain signals, we use a linear combination of discrete Gaussians (LCDG) model having positive and negative components. To accurately account for the large inhomogeneity in infant MRIs, a higher-order Markov-Gibbs Random Field (MGRF) spatial interaction model that integrates third- and fourth- order families with a traditional second-order model is proposed. The proposed approach was tested and evaluated on 102 3D MR brain scans using three metrics: the Dice coefficient, the 95-percentile modified Hausdorff distance, and the absolute brain volume difference. Experimental results show better segmentation of MR brain images compared to current open source segmentation tools.
本文介绍了一种新框架，用于从不同生命阶段的3D磁共振（MR）脑部图像中分割出不同的脑结构（白质、灰质和脑脊液）。所提出的分割框架基于使用一组共对齐训练图像构建的形状先验，该形状先验在分割过程中根据MR图像的一阶和二阶视觉外观特征进行调整。这些特征使用体素级图像强度及其空间交互特征来描述。为了更准确地对脑信号的经验灰度分布进行建模，我们使用了具有正分量和负分量的离散高斯线性组合（LCDG）模型。为了准确考虑婴儿MRI中的大不均匀性，提出了一种高阶马尔可夫 - 吉布斯随机场（MGRF）空间交互模型，该模型将三阶和四阶族与传统的二阶模型集成在一起。使用三个指标在102次3D MR脑部扫描上对所提出的方法进行了测试和评估：骰子系数、95百分位数修正豪斯多夫距离和绝对脑体积差异。实验结果表明，与当前的开源分割工具相比，该方法对MR脑部图像的分割效果更好。
