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Building a surface atlas of hippocampal subfields from high resolution T2-weighted MRI scans using landmark-free surface registration

机译:使用无地标的表面配准通过高分辨率T2加权MRI扫描建立海马亚区表面图集

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The hippocampus is widely studied in neuroimaging field as it plays important roles in memory and learning. However, the critical subfield information is often not explored in most hippocampal studies. We previously proposed a method for hippocampal subfield morphometry by integrating FreeSurfer, FSL, and SPHARM tools. But this method had some limitations, including the analysis of T1-weighted MRI scans without detailed subfield information and hippocampal registration without using important subfield information. To bridge these gaps, in this work, we propose a new framework for building a surface atlas of hippocampal subfields from high resolution T2-weighted MRI scans by integrating state-of-the-art methods for automated segmentation of hippocampal subfields and landmark-free, subfield-aware registration of hippocampal surfaces. Our experimental results have shown the promise of the new framework.
机译:海马在神经影像领域被广泛研究,因为它在记忆和学习中起着重要作用。但是,在大多数海马研究中,通常不探索关键的子领域信息。我们先前提出了一种通过整合FreeSurfer,FSL和SPHARM工具进行海马亚场形态测量的方法。但是这种方法有一些局限性,包括在没有详细子字段信息的情况下对T1加权MRI扫描进行分析,以及在不使用重要子字段信息的情况下进行海马注册。为了弥合这些差距,在这项工作中,我们提出了一个新框架,该框架通过整合先进的海马亚域自动分割方法和无界标的高分辨率T2加权MRI扫描来构建海马亚域表面图集,亚场意识海马表面的配准。我们的实验结果表明了新框架的前景。

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