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首页> 外文期刊>Medical Imaging, IEEE Transactions on >Automated Brain Structure Segmentation Based on Atlas Registration and Appearance Models
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Automated Brain Structure Segmentation Based on Atlas Registration and Appearance Models

机译：基于Atlas配准和外观模型的自动脑结构分割

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摘要

Accurate automated brain structure segmentation methods facilitate the analysis of large-scale neuroimaging studies. This work describes a novel method for brain structure segmentation in magnetic resonance images that combines information about a structure's location and appearance. The spatial model is implemented by registering multiple atlas images to the target image and creating a spatial probability map. The structure's appearance is modeled by a classifier based on Gaussian scale-space features. These components are combined with a regularization term in a Bayesian framework that is globally optimized using graph cuts. The incorporation of the appearance model enables the method to segment structures with complex intensity distributions and increases its robustness against errors in the spatial model. The method is tested in cross-validation experiments on two datasets acquired with different magnetic resonance sequences, in which the hippocampus and cerebellum were segmented by an expert. Furthermore, the method is compared to two other segmentation techniques that were applied to the same data. Results show that the atlas- and appearance-based method produces accurate results with mean Dice similarity indices of 0.95 for the cerebellum, and 0.87 for the hippocampus. This was comparable to or better than the other methods, whereas the proposed technique is more widely applicable and robust.
机译：准确的自动脑结构分割方法可促进大规模神经影像研究的分析。这项工作描述了一种用于磁共振图像中脑结构分割的新方法，该方法结合了有关结构位置和外观的信息。通过将多个地图集图像注册到目标图像并创建空间概率图来实现空间模型。结构的外观由基于高斯比例空间特征的分类器建模。这些组件与贝叶斯框架中的正则化项结合使用，贝叶斯框架使用图割进行全局优化。外观模型的合并使该方法可以分割具有复杂强度分布的结构，并提高其针对空间模型中的错误的鲁棒性。该方法在交叉验证实验中对以不同磁共振序列获得的两个数据集进行了测试，其中专家对海马和小脑进行了分割。此外，将该方法与应用于相同数据的其他两种分割技术进行了比较。结果表明，基于图谱和外观的方法可产生准确的结果，小脑的平均Dice相似度指数为0.95，海马的平均Dice相似度指数为0.87。这可以与其他方法媲美或更好，而所提出的技术则具有更广泛的适用性和鲁棒性。

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