The hippocampus is one of the most studied neuroanatomical structures due to its involvement in attention, learning, and memory as well as its atrophy in ageing, neurological, and psychiatric diseases. Hippocampal shape changes, however, are complex and cannot be fully characterized by a single summary metric such as hippocampal volume as determined from MR images. In this work, we propose an automated, geometry-based approach for the unfolding, point-wise correspondence, and local analysis of hippocampal shape features such as thickness and curvature. Starting from an automated segmentation of hippocampal subfields, we create a 3D tetrahedral mesh model as well as a 3D intrinsic coordinate system of the hippocampal body. From this coordinate system, we derive local curvature and thickness estimates as well as a 2D sheet for hippocampal unfolding. We evaluate the performance of our algorithm with a series of experiments to quantify neurodegenerative changes in Mild Cognitive Impairment and Alzheimer's disease dementia. We find that hippocampal thickness estimates detect known differences between clinical groups and can determine the location of these effects on the hippocampal sheet. Further, thickness estimates improve classification of clinical groups and cognitively unimpaired controls when added as an additional predictor. Comparable results are obtained with different datasets and segmentation algorithms. Taken together, we replicate canonical findings on hippocampal volume/shape changes in dementia, extend them by gaining insight into their spatial localization on the hippocampal sheet, and provide additional, complementary information beyond traditional measures. We provide a new set of sensitive processing and analysis tools for the analysis of hippocampal geometry that allows comparisons across studies without relying on image registration or requiring manual intervention.

翻译：海马体因其参与注意力、学习与记忆功能，以及在衰老、神经系统疾病和精神疾病中出现的萎缩现象，成为神经解剖学中研究最为深入的结构之一。然而，海马体的形态变化极为复杂，无法通过单一汇总指标（如磁共振成像测定的海马体积）完全表征。本研究提出一种基于几何的自动化方法，用于实现海马体展开、逐点对应以及局部形态特征（如厚度和曲率）的分析。通过自动分割海马体亚区，我们构建了三维四面体网格模型及海马体本征三维坐标系。基于该坐标系，我们推导出局部曲率和厚度估计值，并生成用于海马体展开的二维平面。通过一系列实验评估算法性能，我们量化了轻度认知障碍与阿尔茨海默病痴呆中的神经退行性变化。研究发现，海马体厚度估计不仅能检测临床组间的已知差异，还可确定这些效应在海马体展开图上的空间定位。此外，将厚度估计作为额外预测因子加入后，可显著提升临床组与认知正常对照组的分类效果。不同数据集与分割算法的实验结果具有可重复性。综合而言，我们复现了痴呆症中海马体积/形态变化的经典发现，通过揭示其在海马体展开图上的空间分布进一步拓展了认知，并提供了超越传统测量指标的补充信息。本研究为海马体几何分析提供了一套新型灵敏处理与分析工具，无需依赖图像配准或人工干预即可实现跨研究比较。