Cong Shan, Rizkalla Maher, Salama Paul, Risacher Shannon L, West John D, Wu Yu-Chien, Apostolova Liana, Tallman Eileen, Saykin Andrew J, Shen Li
Dept. of Electrical and Computer Engineering, Purdue University West Lafayette, West Lafayette, IN 47907.
Dept. of Electrical and Computer Engineering, Indiana University - Purdue University Indianapolis, Indianapolis, IN 46202.
Conf Proc (Midwest Symp Circuits Syst). 2016 Oct;2016. doi: 10.1109/MWSCAS.2016.7870109. Epub 2017 Mar 6.
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.
海马体在神经成像领域得到了广泛研究，因为它在记忆和学习中发挥着重要作用。然而，在大多数海马体研究中，关键的子区域信息往往未被探索。我们之前提出了一种通过整合FreeSurfer、FSL和SPHARM工具进行海马体子区域形态测量的方法。但该方法存在一些局限性，包括在没有详细子区域信息的情况下分析T1加权MRI扫描，以及在不使用重要子区域信息的情况下进行海马体配准。为了弥补这些差距，在这项工作中，我们提出了一个新框架，通过整合用于海马体子区域自动分割的最新方法和无地标、子区域感知的海马体表面配准方法，从高分辨率T2加权MRI扫描构建海马体子区域的表面图谱。我们的实验结果显示了新框架的前景。
