EXTEND SCOPE & LEVEL OF DETAIL OF ANATOMICAL MODELS FROM IN VIVO DATA

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目及 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 MR数据采集技术的最新发展，部分是由项目2开发的相控阵类型的进步所驱动的，开始产生以前所未有的细节水平显示海马结构解剖特征的图像，为海马子场测量提供基础。由于海马在人类记忆中的作用及其在各种疾病和病症中的意义，通过体内神经成像可靠有效地量化海马子场的能力引起了基础神经科学和临床研究人员的极大兴趣。在P41的当前周期中，我们正在开发一种全自动方法，用于从超高分辨率MRI数据中分割海马子场。使用贝叶斯方法，我们建立了一个计算模型，海马周围的图像是如何产生的，并使用该模型来获得自动分割。我们通过比较我们的分割结果与相应的手动划定在超高分辨率MRI扫描的五个人，我们验证了所提出的技术。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Recent developments in MR data acquisition technology, in part driven by the advances in the types of phased-arrays developed by Project 2, are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify hippocampal subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical researchers. In the current cycle of the P41 we are in the process of developing a fully-automated method for segmenting the hippocampal subfields from ultra-high resolution MRI data. Using a Bayesian approach, we build a computational model of how images around the hippocampus are generated, and use this model to obtain automated segmentations. We validate the proposed technique by comparing our segmentation results with corresponding manual delineations in ultra-high resolution MRI scans of five individuals.