Bayesian longitudinal segmentation of hippocampal substructures in brain MRI using subject-specific atlases.
Bayesian longitudinal segmentation of hippocampal substructures in brain MRI using subject-specific atlases.
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DOI：
10.1016/j.neuroimage.2016.07.020
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发表时间：
2016-11-01
期刊：
影响因子：
5.7
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Iglesias JE;Van Leemput K;Augustinack J;Insausti R;Fischl B;Reuter M;Alzheimer's Disease Neuroimaging Initiative
海马体结构是一个复杂的、异质的结构，由许多不同的、相互作用的亚区组成。这些亚区的萎缩与多种神经退行性疾病有关，最突出的是阿尔茨海默病(AD)。由于MR图像和计算图谱分辨率的提高，在MRI扫描中自动分割海马亚区变得可行。在这里，我们介绍了一个专门的纵向分割的生成性模型，该模型依赖于特定主题的地图集。使用贝叶斯推理联合计算不同时间点的扫描分割。所有时间点都被同等对待，以避免处理偏差。我们使用来自两个公开可用的数据集(ADNI和MIRIAD)的超过4,700次扫描来评估该方法。在重测信度实验中，几乎每个子区域的体积差异显著低于横断面法，骰子重叠率显著高于横断面法(子区域平均：4.5%对6.5%，骰子重叠率：81.8%对75.4%)。纵向算法也显示出对组内差异的敏感性增加：在MIRIAD组(69名受试者：46名AD患者和23名对照组)中，它发现AD和对照组之间的萎缩率存在差异，横断法在一些亚区无法检测到这些差异：右侧下丘旁、左右丘脑前、右侧下丘、左侧齿状回、左侧CA4、左侧HATA和右侧尾部。在ADNI(836例受试者：369例AD，215例早期认知障碍-eMCI-和252例对照组)中，所有方法都发现AD和对照组之间存在显著差异，但所提出的纵向算法检测到对照组和eMCI之间的差异以及eMCI和AD之间的差异，这是横断法无法发现的：左丘前、右丘下部、左右下丘旁、左右HATA。此外，横断面法已经发现的许多差异被检测到了更高的意义。提出的算法将作为开源神经成像包freesurfer的一部分提供。
The hippocampal formation is a complex, heterogeneous structure that consists of a number of distinct, interacting subregions. Atrophy of these subregions is implied in a variety of neurodegenerative diseases, most prominently in Alzheimer’s disease (AD). Thanks to the increasing resolution of MR images and computational atlases, automatic segmentation of hippocampal subregions is becoming feasible in MRI scans. Here we introduce a generative model for dedicated longitudinal segmentation that relies on subject-specific atlases. The segmentations of the scans at the different time points are jointly computed using Bayesian inference. All time points are treated the same to avoid processing bias. We evaluate this approach using over 4,700 scans from two publicly available datasets (ADNI and MIRIAD). In test-retest reliability experiments, the proposed method yielded significantly lower volume differences and significantly higher Dice overlaps than the cross-sectional approach for nearly every subregion (average across subregions: 4.5% vs. 6.5%, Dice overlap: 81.8% vs. 75.4%). The longitudinal algorithm also demonstrated increased sensitivity to group differences: in MIRIAD (69 subjects: 46 with AD and 23 controls), it found differences in atrophy rates between AD and controls that the cross sectional method could not detect in a number of subregions: right parasubiculum, left and right presubiculum, right subiculum, left dentate gyrus, left CA4, left HATA and right tail. In ADNI (836 subjects: 369 with AD, 215 with early cognitive impairment – eMCI – and 252 controls), all methods found significant differences between AD and controls, but the proposed longitudinal algorithm detected differences between controls and eMCI and differences between eMCI and AD that the cross sectional method could not find: left presubiculum, right subiculum, left and right parasubiculum, left and right HATA. Moreover, many of the differences that the cross-sectional method already found were detected with higher significance. The presented algorithm will be made available as part of the open-source neuroimaging package FreeSurfer.
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