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Corpus Callosum segmentation using Deep Neural Networks with Prior information from Multi-atlas images

机译：语料库胼calloSum分割，使用来自多atlas图像的先前信息的深神经网络

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In human brain, Corpus Callosum (CC) is the largest white matter structure, connecting between right and left hemispheres. Structural features such as shape and size of CC in midsagittal plane are of great significance for analyzing various neurological diseases, for example Alzheimer's disease, autism and epilepsy. For quantitative and qualitative studies of CC in brain MR images, robust segmentation of CC is important. In this paper, we present a novel method for CC segmentation. Our approach is based on deep neural networks and the prior information generated from multi-atlas images. Deep neural networks have recently shown good performance in various image processing field. Convolutional neural networks (CNN) have shown outstanding performance for classification and segmentation in medical image fields. We used convolutional neural networks for CC segmentation. Multi-atlas based segmentation model have been widely used in medical image segmentation because atlas has powerful information about the target structure we want to segment, consisting of MR images and corresponding manual segmentation of the target structure. We combined the prior information, such as location and intensity distribution of target structure (i.e. CC), made from multi-atlas images in CNN training process for more improving training. The CNN with prior information showed better segmentation performance than without.
机译：在人类脑中，胼calloSum（Cc）是最大的白质结构，在左右半球之间连接。诸如中间显着平面中CC的形状和大小的结构特征对于分析各种神经疾病，例如阿尔茨海默病，自闭症和癫痫具有重要意义。对于脑MR图像中CC的定量和定性研究，CC的强大分割很重要。在本文中，我们提出了一种用于CC分段的新方法。我们的方法基于深度神经网络和来自多拟标志的图像生成的先前信息。深度神经网络最近在各种图像处理领域中表现出良好的性能。卷积神经网络（CNN）显示出在医学图像领域的分类和分段的出色性能。我们使用了CC分段的卷积神经网络。基于Multi-atlas的分割模型已广泛用于医学图像分割，因为Atlas有关于我们想要段的目标结构的强大信息，由MR图像和目标结构的相应手动分段组成。我们将先前的信息组合，例如目标结构的位置和强度分布（即CC），由CNN培训过程中的多地图集图像进行了更多改进的培训。具有先前信息的CNN显示出的细分性能比没有。

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