...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Medical image analysis >Improved segmentation of white matter tracts with adaptive Riemannian metrics
【24h】

Improved segmentation of white matter tracts with adaptive Riemannian metrics

机译：用自适应黎曼度量改进白质束分割

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

We present a novel geodesic approach to segmentation of white matter tracts from diffusion tensor imaging (DTI). Compared to deterministic and stochastic tractography, geodesic approaches treat the geometry of the brain white matter as a manifold, often using the inverse tensor field as a Riemannian metric. The white matter pathways are then inferred from the resulting geodesics, which have the desirable property that they tend to follow the main eigenvectors of the tensors, yet still have the flexibility to deviate from these directions when it results in lower costs. While this makes such methods more robust to noise, the choice of Riemannian metric in these methods is ad hoc. A serious drawback of current geodesic methods is that geodesics tend to deviate from the major eigenvectors in high-curvature areas in order to achieve the shortest path. In this paper we propose a method for learning an adaptive Riemannian metric from the DTI data, where the resulting geodesics more closely follow the principal eigenvector of the diffusion tensors even in high-curvature regions. We also develop a way to automatically segment the white matter tracts based on the computed geodesics. We show the robustness of our method on simulated data with different noise levels. We also compare our method with tractography methods and geodesic approaches using other Riemannian metrics and demonstrate that the proposed method results in improved geodesics and segmentations using both synthetic and real DTI data.
机译：我们提出了一种从扩散张量成像（DTI）分割白质区域的新颖测地方法。与确定性和随机体检相比，测地学方法通常将反张量场用作黎曼度量，将脑白质的几何学视为流形。然后从所得的短程线中推断出白质路径，该短程线具有理想的特性，即它们倾向于遵循张量的主要特征向量，但在降低成本时仍具有灵活地偏离这些方向的灵活性。尽管这使此类方法对噪声更鲁棒，但是在这些方法中选择黎曼度量是临时的。当前测地线方法的一个严重缺陷是测地线倾向于偏离高曲率区域中的主要特征向量，以实现最短路径。在本文中，我们提出了一种从DTI数据中学习自适应黎曼度量的方法，其中即使在高曲率区域中，生成的测地线也更紧密地遵循扩散张量的主要特征向量。我们还开发了一种基于计算的测地线自动分割白质区域的方法。我们展示了我们的方法对具有不同噪声水平的模拟数据的鲁棒性。我们还将我们的方法与使用其他黎曼度量的线描方法和测地线方法进行了比较，并证明了所提出的方法使用合成DTI数据和实际DTI数据可改善测地线和分割效果。

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