• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Functional imaging and modeling of the heart >Discriminative Joint Context for Automatic Landmark Set Detection from a Single Cardiac MR Long Axis Slice
【24h】

Discriminative Joint Context for Automatic Landmark Set Detection from a Single Cardiac MR Long Axis Slice

机译：从单个心脏MR长轴切片自动识别地标集的判别联合上下文

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

Cardiac magnetic resonance (MR) imaging has advanced to become a powerful diagnostic tool in clinical practice. Automatic detection of anatomic landmarks from MR images is important for structural and functional analysis of the heart. Learning-based object detection methods have demonstrated their capabilities to handle large variations of the object by exploring a local region, context, around the target. Conventional context is associated with each individual landmark to encode local shape and appearance evidence. We extend this concept to a landmark set, where multiple landmarks have connections at the semantic level, e.g., landmarks belonging to the same anatomy. We propose a joint context approach to construct contextual regions between landmarks. A discriminative model is learned to utilize inter-landmark features for landmark set detection as an entirety. This helps resolve ambiguities of individual landmark detection results. A probabilistic boosting tree is used to learn a discriminative model based on contextual features. We adopt a marginal space learning strategy to efficiently learn and search a high dimensional parameter space. A fully automatic system is developed to detect the set of three landmarks of the left ventricle, the apex and the two basal annulus points, from a single cardiac MR long axis image. We test the proposed approach on a database of 795 long axis images from 116 patients. A 4-fold cross validation results show that about 15% reduction of the errors is obtained by integrating joint context into a conventional landmark detection system.
机译：心脏磁共振（MR）成像已成为临床实践中强大的诊断工具。从MR图像自动检测解剖标志对于心脏的结构和功能分析很重要。基于学习的对象检测方法已经证明了它们通过探索目标周围的局部区域，环境来处理对象的较大变化的能力。常规上下文与每个单独的地标相关联，以编码局部形状和外观证据。我们将此概念扩展到一个地标集，其中多个地标在语义级别具有连接，例如，属于同一解剖结构的地标。我们提出了一种联合上下文方法来构造地标之间的上下文区域。学习判别模型以将地标间特征整体用于地标集检测。这有助于解决各个地标检测结果的歧义。概率提升树用于学习基于上下文特征的判别模型。我们采用边际空间学习策略来有效地学习和搜索高维参数空间。开发了一种全自动系统，可从单个心脏MR长轴图像中检测左心室的三个界标，顶点和两个基底环点。我们在来自116位患者的795个长轴图像的数据库上测试了所提出的方法。 4倍交叉验证结果表明，通过将联合上下文集成到常规界标检测系统中，可以减少大约15％的错误。

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