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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从高分辨率T2加权MRI扫描构建海马亚区表面图集

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摘要

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:海马在记忆和学习中起着重要作用,因此在神经影像领域被广泛研究。但是,在大多数海马研究中通常不探索关键的子领域信息。我们之前通过整合FreeSurfer,FSL和SPHARM工具提出了海马亚场形态测量方法。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配准。为了弥合这些差距,在这项工作中,我们提出了一个新框架,该框架通过整合最先进的方法对海马亚区进行自动分割和不使用界标,通过高分辨率的T2加权MRI扫描来构建海马亚区的表面图集,亚场意识的海马表面配准。我们的实验结果表明了新框架的前景。

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