NEUROANATOMICAL SEGMENTATION

该项目将解决将MRI脑体积分割为 标记区域，以使每个大脑体素具有一个或多个解剖结构 标签，每个标签具有相关联的标签概率索引。软件工具 将被开发为允许手动标记两种类型的体素 图像卷(A)来自人类身体脑的冷冻宏体数据和(B) 正常受试者的MRI体积。这些工具将应用于 这两种数据类型的子集，以生成后续数据的参考数据 自动化程序。第二，我们将继续现有的项目， 计算机化分割，最大限度地减少了调查人员的参与。 整个分割程序，手动和自动的，将在 通过识别主要区域依次对解剖细节进行更精细的刻度， 例如额叶，朝向特定的结构，例如丘脑。更精细 标签将手动生成。解剖学的每一个新层次 规模化将需要开发更精细的算法和更多 详细的模型。在每个周期结束时，我们将进行重新- 计算机辅助技术的准确度和精密度评价 与从所获取的子集手动导出的结果相比 数据。将生成幻影数据以评估 结果针对注入的图像噪声、对比度级别的变化 组织和切片厚度之间的关系。该项目具有以下内容 具体目标：a)建立神经解剖学的等级分类 从粗略到精细的结构。B)收集轴向高分辨率、高分辨率 对比来自同质人群的MRI数据。将这些数据与 加州大学洛杉矶分校三维图像在立体定位空间中的应用 翘曲软件。C)利用现有工具并开发更多工具 计算机辅助手动标记体积中的每个体素，使用 在区域标签的层次之上。将这些技术应用于 要生成标记的冷冻微体数据和MRI数据的子集 音量。标记的数据将用于与和AS进行比较 自动化技术的起步模型。D)扩展我们现有的 大体组织区域的自动分割算法 分类、基于模型的区域分割和皮质标记。 E)对组织分类算法进行验证实验 有着几何形状和类似大脑的幻影。F)应用自动分割 对数据量进行二次抽样和进行统计的技术 与手工方法的结果进行比较。

This project will address the segmentation of MRI brain volumes into labelled regions such that each cerebral voxel has one or more anatomical labels, each with an associated label probability index. Software tools will be developed to allow manual labelling of voxels in two types of image volumes (a) cryomacrotome data from human cadaver brains and (b) MRI volumes from normal subjects. These tools will be applied to a subset of both data types to generate reference data for subsequent automated procedures. Second, we will continue existing projects for computerized segmentation with a minimum of investigator involvement. The overall segmentation program, manual and automated, will develop at successively finer scales of anatomic detail by identifying major zones, e.g., frontal lobe, toward specific structures, e.g., thalamus. Finer labelling will be generated manually. Each new level of anatomical scale will require the development of more refined algorithms and more detailed models. At the completion of each cycle, we will conduct a re- evaluation of the accuracy and precision of the computerized techniques as compared with manually-derived results from a subset of the acquired data. Phantom data will be generated to assess the robustness of the results against injected image noise, variations in contrast level between tissues and slice thickness. The project has the following specific aims: a) Establish a hierarchical classification of neuroanatomy from gross to fine structure. b) Collect axial high-resolution, high- contrast MRI data from a homogeneous population. Combine these data with UCLA cryomacrotome data in stereotaxic space by application of 3-D image warping software. c) Use existing and develop further tools for computer-aided manual labelling of every voxel in a volume, using the above hierarchy for regional labelling. Apply these techniques to a subset of both cryomacrotome data and MRI data to generate labelled volumes. The labelled data will be used both for comparison with and as a starting model for automated techniques. d) Extend our existing automated segmentation algorithms in the areas of gross tissue classification, model-based regional parcellation and cortical labelling. e) Conduct validation experiments for tissue classification algorithms with geometric and brain-like phantoms. f) Apply automated segmentation techniques to sub-sample of data volumes and conduct statistical comparisons with results with manual methods.