• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Conference on image processing >Improving whole-brain segmentations through incorporating regional image intensity statistics
【24h】

Improving whole-brain segmentations through incorporating regional image intensity statistics

机译：通过纳入区域形象强度统计来改善全脑细分

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

摘要

Multi-atlas segmentation methods are among the most accurate approaches for the automatic labeling of magnetic resonance (MR) brain images. The individual segmentations obtained through multi-atlas propagation can be combined using an unweighted or locally weighted fusion strategy. Label overlaps can be further improved by refining the label sets based on the image intensities using the Expectation-Maximisation (EM) algorithm. A drawback of these approaches is that they do not consider knowledge about the statistical intensity characteristics of a certain anatomical structure, especially its intensity variance. In this work we employ learned characteristics of the intensity distribution in various brain regions to improve on multi-atlas segmentations. Based on the intensity profile within labels in a training set, we estimate a normalized variance error for each structure. The boundaries of a segmented region are then adjusted until its intensity characteristics are corrected for this variance error observed in the training sample. Specifically, we start with a high-probability "core" segmentation of a structure, and maximise the similarity with the expected intensity variance by enlarging it. We applied the method to 35 datasets of the OASIS database for which manual segmentations into 138 regions are available. We assess the resulting segmentations by comparison with this gold-standard, using overlap metrics. Intensity-based statistical correction improved similarity indices (SI) compared with EM-refined multi-atlas propagation from 75.6% to 76.2% on average. We apply our novel correction approach to segmentations obtained through either a locally weighted fusion strategy or an EM-based method and show significantly increased similarity indices.
机译：多拟标志分段方法是磁共振（MR）脑图像自动标记的最准确的方法之一。可以使用多标志性传播获得的各个分割，使用未加权或局部加权的融合策略组合。通过使用期望最大化（EM）算法基于图像强度来改进标签集，可以进一步提高标签重叠。这些方法的缺点是他们不考虑关于某种解剖结构的统计强度特征的知识，特别是其强度方差。在这项工作中，我们雇用了各种大脑区域中强度分布的特征，以改善多地图集分割。基于训练集中标签内的强度配置文件，我们估计每个结构的归一化方差误差。然后调整分段区域的边界直到在训练样本中观察到的这种方差误差校正其强度特征。具体地，我们从结构的高概率“核心”分割开始，并通过放大它来最大化与预期强度方差的相似性。我们将该方法应用于OASIS数据库的35个数据集，其中有一个手动分段为138个区域。通过使用重叠度量，我们通过比较来评估结果分割。基于强度的统计校正改善了相似性指数（SI）与EM-Refined Multi-Atlas传播平均值为75.6％至76.2％。我们将我们的小说校正方法应用于通过局部加权融合策略或基于EM的方法获得的分段，并显示出显着增加的相似指数。

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