...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Medical image analysis >Global localization of 3D anatomical structures by pre-filtered Hough Forests and discrete optimization
【24h】

Global localization of 3D anatomical structures by pre-filtered Hough Forests and discrete optimization

机译：通过预过滤的Hough森林和离散优化的全球3D解剖结构定位

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

The accurate localization of anatomical landmarks is a challenging task, often solved by domain specific approaches. We propose a method for the automatic localization of landmarks in complex, repetitive anatomical structures.The key idea is to combine three steps: (1) a classifier for pre-filtering anatomical landmark positions that (2) are refined through a Hough regression model, together with (3) a parts-based model of the global landmark topology to select the final landmark positions. During training landmarks are annotated in a set of example volumes. A classifier learns local landmark appearance, and Hough regressors are trained to aggregate neighborhood information to a precise landmark coordinate position. A non-parametric geometric model encodes the spatial relationships between the landmarks and derives a topology which connects mutually predictive landmarks. During the global search we classify all voxels in the query volume, and perform regression-based agglomeration of landmark probabilities to highly accurate and specific candidate points at potential landmark locations. We encode the candidates' weights together with the conformity of the connecting edges to the learnt geometric model in a Markov Random Field (MRF). By solving the corresponding discrete optimization problem, the most probable location for each model landmark is found in the query volume.We show that this approach is able to consistently localize the model landmarks despite the complex and repetitive character of the anatomical structures on three challenging data sets (hand radiographs, hand CTs, and whole body CTs), with a median localization error of 0.80 mm, 1.19 mm and 2.71 mm, respectively.
机译：解剖学标志性的准确本地化是一个具有挑战性的任务，通常通过域特定方法解决。我们提出了一种方法，用于复杂，重复解剖结构中地标的地标的方法。关键的想法是组合三个步骤：（1）用于通过Hough回归模型改进（2）的预过滤解剖学地标位置的分类器，与（3）全球地标拓扑的基于零件的模型，以选择最终的地标位置。在训练期间，地标在一组示例卷中注释。分类器学习当地地标外观，并且训练霍夫回归器被培训以将邻域信息聚合到精确的地标坐标位置。非参数几何模型对地标之间的空间关系进行了编码，并导出连接互联性地标的拓扑。在全球搜索期间，我们将所有体素分类在查询卷中，并在潜在地标位置进行高度准确和特定的候选点，对地标概率进行基于回归的集聚。我们将候选权重与连接边的整合与Markov随机字段（MRF）中的学习几何模型一起编码。通过解决相应的离散优化问题，在查询卷中找到每个模型地标的最可能的位置。我们表明这种方法能够始终如一地本地化模型地标，尽管在三个具有挑战性的数据上的解剖结构的复杂性和重复特征，但是在三个具有挑战性的数据上的复杂性和重复的特征设置（手动射线照相，手CTS和全身CTS），中位数定位误差分别为0.80毫米，1.19毫米和2.71毫米。

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