• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>SPIE Medical Imaging Conference >Corpus Callosum segmentation using Deep Neural Networks with Prior information from Multi-atlas images
【24h】

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

机译：使用深度神经网络结合多图集图像中的先验信息进行Call体分割

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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：在人脑中，Corpus Callosum（CC）是最大的白质结构，连接左右半球。矢状面中CC的形状和大小等结构特征对于分析各种神经系统疾病（例如阿尔茨海默氏病，自闭症和癫痫病）具有重要意义。对于大脑MR图像中CC的定量和定性研究，CC的鲁棒分割很重要。在本文中，我们提出了一种新的CC分割方法。我们的方法基于深度神经网络和从多图集图像生成的先验信息。深度神经网络最近在各种图像处理领域显示出良好的性能。卷积神经网络（CNN）在医学图像领域中表现出出色的分类和分割性能。我们使用卷积神经网络进行CC分割。基于多图集的分割模型已广泛用于医学图像分割中，因为图集具有有关我们要分割的目标结构的强大信息，包括MR图像和目标结构的相应手动分割。我们在CNN训练过程中结合了由多图集图像制成的目标信息（例如目标结构（即CC）的位置和强度分布）的先验信息，以进一步改善训练效果。具有先验信息的CNN表现出比没有信息更好的分割性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号