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利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 磁共振数据采集技术的最新发展，在一定程度上受到项目2开发的相控阵类型的进步的推动，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。由于海马体在人类记忆中的作用及其在各种疾病和疾病中的作用，通过活体神经成像可靠而有效地量化海马亚区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，我们正在开发一种从超高分辨率MRI数据中分割海马区的全自动方法。使用贝叶斯方法，我们建立了一个计算模型，说明海马体周围的图像是如何生成的，并使用该模型来获得自动分割。我们通过将我们的分割结果与五个人的超高分辨率MRI扫描中相应的手动描绘进行比较来验证所提出的技术。

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.