基于ITK多图谱配准的脑部皮质下核团的分割研究

doi:10.3969/j.issn.0258-8021.2017.05.003

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Abstract Numerous studies indicated that the process of Alzheimer's disease (AD) was closely correlated with atrophy of subcortical structures. Atrophy of some subcortical structures like hippocampi, could be a biomarker of early diagnosis of AD, which makes the segmentation of subcortical structures very important. Based on 3DT1W-MR images of 30 AD and 30 normal controls, we firstly performed brain tissue extraction combining histogram analysis and three-dimensional morphological processing method, then registered 10 brain atlas to the preprocessed image based on ITK registration framework. A two-stage images registration was applied to register multiple atlas on the subject MR scan. First, we applied affine registration with mean squares as metric. Second, B-spline transform based on mutual information model was applied to further registration. In the two-stage registration method, linear interpolation model and the optimizer named regular step gradient descent were used. After registration, STAPLE algorithm was achieved to perform image fusion on the obtained 10 registered images to get the final segmented image. The results show that all volumes of subcortical structures except caudate show no statistical differences between our method and FSL-FIRST algorithm (P>0.05); bilateral hippocampus and right nucleus accumbens suffer atrophy in AD (P<0.05). Our experimental data shows the validity of the multi-atlas-based segmentation method on segmentation of the brain subcortical structures.

Key words： subcortical structure multi-atlas segmentation image registration ITK

Received: 23 December 2016

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R318

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	Articles by authors
	Nie Xiuling
	Liu Renyuan
	Lu Jiaming
	Zhang Bing
	Sun Yu
	Wan Suiren

Cite this article:

Nie Xiuling,Liu Renyuan,Lu Jiaming, et al. Multi-atlas Segmentation of Brain Subcortical Structures Based on ITK Registration Framework[J]. Chinese Journal of Biomedical Engineering, 2017, 36(5): 529-535.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2017.05.003 OR http://cjbme.csbme.org/EN/Y2017/V36/I5/529

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