...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neuroinformatics >Fast and Precise Hippocampus Segmentation Through Deep Convolutional Neural Network Ensembles and Transfer Learning
【24h】

Fast and Precise Hippocampus Segmentation Through Deep Convolutional Neural Network Ensembles and Transfer Learning

机译：通过深度卷积神经网络集合和转移学习快速和精确的海马分割

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Automatic segmentation of the hippocampus from 3D magnetic resonance imaging mostly relied on multi-atlas registration methods. In this work, we exploit recent advances in deep learning to design and implement a fully automatic segmentation method, offering both superior accuracy and fast result. The proposed method is based on deep Convolutional Neural Networks (CNNs) and incorporates distinct segmentation and error correction steps. Segmentation masks are produced by an ensemble of three independent models, operating with orthogonal slices of the input volume, while erroneous labels are subsequently corrected by a combination of Replace and Refine networks. We explore different training approaches and demonstrate how, in CNN-based segmentation, multiple datasets can be effectively combined through transfer learning techniques, allowing for improved segmentation quality. The proposed method was evaluated using two different public datasets and compared favorably to existing methodologies. In the EADC-ADNI HarP dataset, the correspondence between the method's output and the available ground truth manual tracings yielded a mean Dice value of 0.9015, while the required segmentation time for an entire MRI volume was 14.8 seconds. In the MICCAI dataset, the mean Dice value increased to 0.8835 through transfer learning from the larger EADC-ADNI HarP dataset.
机译：从3D磁共振成像自动分割海马主要依赖于多标准注册方法。在这项工作中，我们利用深度学习的最近进步设计和实施全自动分割方法，提供卓越的准确性和快速结果。该方法基于深度卷积神经网络（CNNS），并包含不同的分割和纠错步骤。分割掩模由三个独立模型的集合生产，与输入体积的正交切片操作，而替换和细化网络的组合随后纠正了错误的标签。我们探讨了不同的培训方法，并演示如何在基于CNN的分割中，通过传输学习技术可以有效地组合多个数据集，从而允许改善的分割质量。使用两种不同的公共数据集评估所提出的方法，并对现有方法进行比较。在EADC-ADNI HARP数据集中，方法的输出与可用地面真相之间的对应关系产生了0.9015的平均骰子值，而整个MRI体积的所需分段时间为14.8秒。在Miccai DataSet中，通过从较大的EADC-ADNI HARP数据集转移学习，平均骰子值增加到0.8835。

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