Van Leemput Koen, Bakkour Akram, Benner Thomas, Wiggins Graham, Wald Lawrence L, Augustinack Jean, Dickerson Bradford C, Golland Polina, Fischl Bruce
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, MGH, Harvard Medical School, USA.
Med Image Comput Comput Assist Interv. 2008;11(Pt 1):235-43. doi: 10.1007/978-3-540-85988-8_29.
Recent developments in MR data acquisition technology 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 its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. In this paper, we propose a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution MRI data. Using a Bayesian approach, we build a computational model of how images around the hippocampal area 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.
磁共振数据采集技术的最新进展开始产生能够以前所未有的细节水平显示海马结构解剖特征的图像，为海马亚区测量提供了基础。由于海马体在人类记忆中的作用及其在多种疾病和病症中的影响，通过体内神经成像可靠且有效地量化其亚区的能力对基础神经科学和临床研究都具有极大的吸引力。在本文中，我们提出了一种用于在超高分辨率MRI数据中分割海马亚区的全自动方法。我们采用贝叶斯方法构建了一个关于海马区域周围图像如何生成的计算模型，并使用该模型获得自动分割结果。我们通过将分割结果与五名个体的超高分辨率MRI扫描中的相应手动描绘进行比较，来验证所提出的技术。
