Stratified mixture modeling for segmentation of white-matter lesions in brain MR images
Stratified mixture modeling for segmentation of white-matter lesions in brain MR images
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DOI：
10.1016/j.neuroimage.2015.09.047
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发表时间：
2016-01-01
期刊：
影响因子：
5.7
通讯作者：
Spiclin, Ziga
中科院分区：
文献类型：
--
作者：
Galimzianova, Alfiia;Pernus, Franjo;Spiclin, Ziga
从磁共振(MR)图像中准确地描述白质病变对于某些神经系统疾病的诊断和治疗越来越重要，并且可以通过自动分割病变来客观有效地执行。这通常涉及到对全脑MR强度分布进行建模，然而，捕获MR强度变异性和病变异质性的各种来源会导致高度复杂的全脑MR强度模型，因此在大量MR图像上的稳健估计是一个巨大的挑战。我们提出了一种采用分层混合建模的新方法，其主要前提是可以将原本复杂的全脑模型简化为小脑子区域中易于处理的参数形式。我们在具有不同病变负荷的多发性硬化症(MS)患者的MR图像上显示，即使在存在病变的情况下，稳健的估计器也能够对小脑区的MR强度进行准确的混合建模。混合模型跨地层的重新组合提供了准确的全脑MR强度模型。增加子区域的数量，从而增加模型的复杂性，一致地提高了全脑MR强度建模和正常结构分割的准确性。提出的方法被结合到三种无监督的病变分割方法中，与原始和其他三种最先进的方法相比，所提出的建模方法显著改善了病变分割，因为在MS(C)2015 Elsevier Inc.的30例患者的真实MR图像上，Dice相似性指数增加和假阳性数减少。
Accurate characterization of white-matter lesions from magnetic resonance (MR) images has increasing importance for diagnosis and management of treatment of certain neurological diseases, and can be performed in an objective and effective way by automated lesion segmentation. This usually involves modeling the whole-brain MR intensity distribution, however, capturing various sources of MR intensity variability and lesion heterogeneity results in highly complex whole-brain MR intensity models, thus their robust estimation on a large set of MR images presents a huge challenge. We propose a novel approach employing stratified mixture modeling, where the main premise is that the otherwise complex whole-brain model can be reduced to a tractable parametric formin small brain subregions. We show on MR images of multiple sclerosis (MS) patients with different lesion loads that robust estimators enable accurate mixture modeling of MR intensity in small brain subregions even in the presence of lesions. Recombination of the mixture models across strata provided an accurate whole-brain MR intensity model. Increasing the number of subregions and, thereby, the model complexity, consistently improved the accuracy of whole-brain MR intensity modeling and segmentation of normal structures. The proposed approach was incorporated into three unsupervised lesion segmentation methods and, compared to original and three other state-of-the-art methods, the proposed modeling approach significantly improved lesion segmentation according to increased Dice similarity indices and lower number of false positives on real MR images of 30 patients with MS. (C) 2015 Elsevier Inc. All rights reserved.