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，部分是由相控阵类型的进步推动的 由Project 2开发，开始产生显示海马体结构解剖特征的图像 前所未有的细节水平，为海马子场测量提供了基础。因为这个角色是 海马体在人类记忆中的作用及其在各种障碍和条件下，可靠和 通过活体神经成像有效地量化海马亚区对于基础神经科学和 临床研究人员。在P41的当前周期中，我们正在开发一种完全自动化的方法来 从超高分辨率核磁共振数据中分割出海马区。使用贝叶斯方法，我们构建了一个 如何生成海马体周围图像的计算模型，并使用该模型获得自动 分段。通过将分割结果与相应的人工操作结果进行比较，验证了该方法的有效性 五个人的超高分辨率核磁共振扫描的轮廓。

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.