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4D Segmentation of Brain MR Images with Constrained Cortical Thickness Variation

机译:受限皮层厚度变化的脑MR图像的4D分割

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摘要

Segmentation of brain MR images plays an important role in longitudinal investigation of developmental, aging, disease progression changes in the cerebral cortex. However, most existing brain segmentation methods consider multiple time-point images individually and thus cannot achieve longitudinal consistency. For example, cortical thickness measured from the segmented image will contain unnecessary temporal variations, which will affect the time related change pattern and eventually reduce the statistical power of analysis. In this paper, we propose a 4D segmentation framework for the adult brain MR images with the constraint of cortical thickness variations. Specifically, we utilize local intensity information to address the intensity inhomogeneity, spatial cortical thickness constraint to maintain the cortical thickness being within a reasonable range, and temporal cortical thickness variation constraint in neighboring time-points to suppress the artificial variations. The proposed method has been tested on BLSA dataset and ADNI dataset with promising results. Both qualitative and quantitative experimental results demonstrate the advantage of the proposed method, in comparison to other state-of-the-art 4D segmentation methods.
机译:大脑MR图像的分割在纵向研究大脑皮层的发育,衰老,疾病进展变化中起重要作用。但是,大多数现有的脑分割方法分别考虑多个时间点图像,因此无法实现纵向一致性。例如,从分割图像测量的皮层厚度将包含不必要的时间变化,这将影响与时间相关的变化模式,并最终降低分析的统计能力。在本文中,我们提出了具有皮质厚度变化约束的成人脑MR图像的4D分割框架。具体而言,我们利用局部强度信息来解决强度不均匀性,空间皮层厚度约束以将皮层厚度保持在合理范围内,以及利用邻近时间点的时间皮层厚度变化约束来抑制人为变化。该方法已在BLSA数据集和ADNI数据集上进行了测试,结果令人满意。与其他最新的4D分割方法相比,定性和定量实验结果都证明了该方法的优势。

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