Hu Shiyan, Coupé Pierrick, Pruessner Jens C, Collins D Louis
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
Hum Brain Mapp. 2014 Feb;35(2):377-95. doi: 10.1002/hbm.22183. Epub 2012 Sep 15.
The human medial temporal lobe (MTL) is an important part of the limbic system, and its substructures play key roles in learning, memory, and neurodegeneration. The MTL includes the hippocampus (HC), amygdala (AG), parahippocampal cortex (PHC), entorhinal cortex, and perirhinal cortex--structures that are complex in shape and have low between-structure intensity contrast, making them difficult to segment manually in magnetic resonance images. This article presents a new segmentation method that combines active appearance modeling and patch-based local refinement to automatically segment specific substructures of the MTL including HC, AG, PHC, and entorhinal/perirhinal cortex from MRI data. Appearance modeling, relying on eigen-decomposition to analyze statistical variations in image intensity and shape information in study population, is used to capture global shape characteristics of each structure of interest with a generative model. Patch-based local refinement, using nonlocal means to compare the image local intensity properties, is applied to locally refine the segmentation results along the structure borders to improve structure delimitation. In this manner, nonlocal regularization and global shape constraints could allow more accurate segmentations of structures. Validation experiments against manually defined labels demonstrate that this new segmentation method is computationally efficient, robust, and accurate. In a leave-one-out validation on 54 normal young adults, the method yielded a mean Dice κ of 0.87 for the HC, 0.81 for the AG, 0.73 for the anterior parts of the parahippocampal gyrus (entorhinal and perirhinal cortex), and 0.73 for the posterior parahippocampal gyrus.
人类内侧颞叶（MTL）是边缘系统的重要组成部分，其亚结构在学习、记忆和神经退行性变中起关键作用。MTL包括海马体（HC）、杏仁核（AG）、海马旁回皮质（PHC）、内嗅皮质和嗅周皮质——这些结构形状复杂，结构间强度对比度低，使得在磁共振图像中手动分割它们很困难。本文提出了一种新的分割方法，该方法结合了主动外观模型和基于补丁的局部细化，以从MRI数据中自动分割MTL的特定亚结构，包括HC、AG、PHC以及内嗅/嗅周皮质。外观模型依靠特征分解来分析研究人群中图像强度和形状信息的统计变化，用于通过生成模型捕获每个感兴趣结构的全局形状特征。基于补丁的局部细化使用非局部均值来比较图像局部强度属性，用于沿结构边界局部细化分割结果，以改善结构界定。通过这种方式，非局部正则化和全局形状约束可以实现更准确的结构分割。针对手动定义标签的验证实验表明，这种新的分割方法计算效率高、稳健且准确。在对54名正常年轻成年人进行的留一法验证中，该方法对HC的平均Dice κ为0.87，对AG为0.81，对海马旁回前部（内嗅和嗅周皮质）为0.73，对海马旁回后部为0.73。
