...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neuroinformatics >FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation
【24h】

FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation

机译：基于FCN的Multi-Atlas引导器器官分割的标签校正

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

Segmentation of medical images using multiple atlases has recently gained immense attention due to their augmented robustness against variabilities across different subjects. These atlas-based methods typically comprise of three steps: atlas selection, image registration, and finally label fusion. Image registration is one of the core steps in this process, accuracy of which directly affects the final labeling performance. However, due to inter-subject anatomical variations, registration errors are inevitable. The aim of this paper is to develop a deep learning-based confidence estimation method to alleviate the potential effects of registration errors. We first propose a fully convolutional network (FCN) with residual connections to learn the relationship between the image patch pair (i.e., patches from the target subject and the atlas) and the related label confidence patch. With the obtained label confidence patch, we can identify the potential errors in the warped atlas labels and correct them. Then, we use two label fusion methods to fuse the corrected atlas labels. The proposed methods are validated on a publicly available dataset for hippocampus segmentation. Experimental results demonstrate that our proposed methods outperform the state-of-the-art segmentation methods.
机译：使用多个地图集的医学图像的分割最近引起了巨大的关注，因为它们在不同主题的可变性性的增强稳健性稳健性。基于地图集的方法通常包括三个步骤：Atlas选择，图像配准，最后标记融合。图像配准是此过程中的核心步骤之一，其准确性直接影响最终标记性能。然而，由于对象间解剖变化，登记误差是不可避免的。本文的目的是开发一种基于深入的学习置信估计方法，以减轻登记误差的潜在影响。我们首先提出一个完全卷积的网络（FCN），以剩余连接来学习图像补片对（即，来自目标主题的补丁以及Atlas的补丁）和相关标签置信贴片之间的关系。通过所获得的标签置信贴片，我们可以识别翘曲的地图集标签中的潜在错误并纠正它们。然后，我们使用两个标签融合方法来熔断纠正的阿特拉斯标签。所提出的方法在公共可用数据集上验证了海马分割。实验结果表明，我们所提出的方法优于最先进的分段方法。

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