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Metric Learning for Multi-atlas based Segmentation of Hippocampus

机译：基于Multi-Atlas的公制学习海马分割

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Automatic and reliable segmentation of hippocampus from MR brain images is of great importance in studies of neurological diseases, such as epilepsy and Alzheimer's disease. In this paper, we proposed a novel metric learning method to fuse segmentation labels in multi-atlas based image segmentation. Different from current label fusion methods that typically adopt a predefined distance metric model to compute a similarity measure between image patches of atlas images and the image to be segmented, we learn a distance metric model from the atlases to keep image patches of the same structure close to each other while those of different structures are separated. The learned distance metric model is then used to compute the similarity measure between image patches in the label fusion. The proposed method has been validated for segmenting hippocampus based on the EADC-ADNI dataset with manually labelled hippocampus of 100 subjects. The experiment results demonstrated that our method achieved statistically significant improvement in segmentation accuracy, compared with state-of-the-art multi-atlas image segmentation methods.
机译：来自MR脑形象的海马的自动和可靠分割对于神经疾病的研究，例如癫痫和阿尔茨海默病的研究非常重要。在本文中，我们提出了一种基于多标准的图像分割中的熔断器分割标签的新型度量学习方法。不同于通常采用预定义距离度量模型的当前标签融合方法来计算ATLAS图像的图像斑块与要分段的图像之间的相似性测量，我们从atlase学习一个距离度量模型，以保持相同结构的图像补丁关闭彼此分开，而不同结构的彼此。然后使用学习距离度量模型来计算标签融合中的图像补丁之间的相似性度量。所提出的方法已经基于EADC-ADNI数据集进行了分割海马，该数据集具有100个受试者的手动标记的海马。实验结果表明，与最先进的多阿特拉斯图像分割方法相比，我们的方法在分割精度上实现了分割精度的统计显着提高。

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