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一种用于在解剖学磁共振成像体积中对皮质层进行拓扑正确分割的高效算法。

An efficient algorithm for topologically correct segmentation of the cortical sheet in anatomical mr volumes.

作者信息

Kriegeskorte N, Goebel R

机构信息

Faculty of Psychology, Department of Cognitive Neuroscience, Universiteit Maastricht, Universiteitssingel 40, Maastricht, 6229 ET, The Netherlands.

出版信息

Neuroimage. 2001 Aug;14(2):329-46. doi: 10.1006/nimg.2001.0831.

Abstract

Polygon-mesh representations of the cortices of individual subjects are of anatomical interest, aid visualization of functional imaging data and provide important constraints for their statistical analysis. Due to noise and partial volume sampling, however, conventional segmentation methods rarely yield a voxel object whose outer boundary represents the folded cortical sheet without topological errors. These errors, called handles, have particularly deleterious effects when the polygon mesh constructed from the segmented voxel representation is inflated or flattened. So far handles had to be removed by cumbersome manual editing, or the computationally more expensive method of reconstruction by morphing had to be used, incorporating the a priori constraint of simple topology into the polygon-mesh model. Here we describe a linear time complexity algorithm that automatically detects and removes handles in presegmentations of the cortex obtained by conventional methods. The algorithm's modifications reflect the true structure of the cortical sheet. The core component of our method is a region growing process that starts deep inside the object, is prioritized by the distance-to-surface of the voxels considered for inclusion and is selftouching-sensitive, i.e., voxels whose inclusion would add a handle are never included. The result is a binary voxel object identical to the initial object except for "cuts" located in the thinnest part of each handle. By applying the same method to the inverse object, an alternative set of solutions is determined, correcting the errors by addition instead of deletion of voxels. For each handle separately, the solution more consistent with the intensities of the original anatomical MR scan is chosen. The accuracy of the resulting polygon-mesh reconstructions has been validated by visual inspection, by quantitative comparison to an expert's manual corrections, and by crossvalidation between reconstructions from different scans of the same subject's cortex.

摘要

个体受试者皮质的多边形网格表示具有解剖学意义,有助于功能成像数据的可视化,并为其统计分析提供重要约束。然而,由于噪声和部分容积采样,传统的分割方法很少能产生一个体素对象,其外边界能代表无拓扑错误的折叠皮质层。这些错误称为把柄,当从分割的体素表示构建的多边形网格被膨胀或展平时,会产生特别有害的影响。到目前为止,把柄必须通过繁琐的手动编辑来去除,或者必须使用计算成本更高的通过变形进行重建的方法,将简单拓扑的先验约束纳入多边形网格模型。在这里,我们描述了一种线性时间复杂度算法,该算法能自动检测并去除通过传统方法获得的皮质预分割中的把柄。该算法的修改反映了皮质层的真实结构。我们方法的核心组件是一个区域生长过程,它从对象内部深处开始,根据考虑纳入的体素到表面的距离进行优先级排序,并且对自接触敏感,即永远不会纳入那些纳入后会增加把柄的体素。结果是一个二值体素对象,除了位于每个把柄最薄部分的“切口”外,与初始对象相同。通过将相同方法应用于反对象,确定了一组替代解决方案,通过添加而不是删除体素来纠正错误。对于每个把柄分别选择与原始解剖学磁共振扫描强度更一致的解决方案。通过视觉检查、与专家手动校正的定量比较以及同一受试者皮质不同扫描重建之间的交叉验证,验证了所得多边形网格重建的准确性。

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