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Shape-based Multifeature Brain Parcellation

机译:基于形状的多功能脑碎裂

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We present a novel approach to parcellate - delineate the anatomical feature (folds, gyri. sulci) boundaries - the brain cortex. Our approach is based on extracting the 3D brain cortical surface mesh from magnetic resonance (MR) images, computing the shape measures (area, mean curvature, geodesic, and travel depths) for this mesh, and delineating the anatomical feature boundaries using these measures. We use angle-area preserving mapping of the cortical surface mesh to a simpler topology (disk or rectangle) to aid in the visualization and delineation of these boundaries. Contrary to commonly used generic 2D brain image atlas-based approaches, we use 3D surface mesh data extracted from a given brain MR imaging data and its specific shape measures for the parcellation. Our method does not require any non-linear registration of a given brain dataset to a generic atlas and hence, does away with the structure similarity assumption critical to the atlas-based approaches. We evaluate our approacli using Mindboggle manually labeled brain datasets and achieve the following accuracies: 72.4% for gyri, 78.5% for major sulci, and 98.4% for folds. These results warrant further investigation of this approach as an alternative or as an initialization to the atlas-based approaches.
机译:我们提出了一种新颖的方法-描绘大脑皮层的解剖特征(褶皱,回旋神经)边界。我们的方法基于从磁共振(MR)图像中提取3D脑皮质表面网格,计算该网格的形状度量(面积,平均曲率,测地线和行进深度),并使用这些度量来描绘解剖特征边界。我们使用将皮质表面网格保留角度区域的映射到更简单的拓扑(磁盘或矩形)来帮助可视化和描绘这些边界。与通常使用的基于通用2D脑图像图集的方法相反,我们使用从给定脑MR图像数据及其特定形状度量中提取的3D表面网格数据进行分割。我们的方法不需要将给定的脑部数据集进行任何非线性配准到通用图集,因此不需要使用基于图集的方法至关重要的结构相似性假设。我们使用Mindboggle手动标记的大脑数据集评估了方法的准确性,并实现了以下准确性:gyri为72.4%,主要沟为78.5%,褶皱为98.4%。这些结果保证了对该方法的进一步研究,作为基于图集的方法的替代方法或初始化方法。

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