...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Medical Imaging >Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration
【24h】

Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration

机译：通过将正则化的对应点整合到密集的可变形配准中来估算肺部CT的大运动

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

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

摘要

We present a novel algorithm for the registration of pulmonary CT scans. Our method is designed for large respiratory motion by integrating sparse keypoint correspondences into a dense continuous optimization framework. The detection of keypoint correspondences enables robustness against large deformations by jointly optimizing over a large number of potential discrete displacements, whereas the dense continuous registration achieves subvoxel alignment with smooth transformations. Both steps are driven by the same normalized gradient fields data term. We employ curvature regularization and a volume change control mechanism to prevent foldings of the deformation grid and restrict the determinant of the Jacobian to physiologically meaningful values. Keypoint correspondences are integrated into the dense registration by a quadratic penalty with adaptively determined weight. Using a parallel matrix-free derivative calculation scheme, a runtime of about 5 min was realized on a standard PC. The proposed algorithm ranks first in the EMPIRE10 challenge on pulmonary image registration. Moreover, it achieves an average landmark distance of 0.82 mm on the DIR-Lab COPD database, thereby improving upon the state of the art in accuracy by 15%. Our algorithm is the first to reach the inter-observer variability in landmark annotation on this dataset.
机译：我们提出了一种新颖的算法，用于肺部CT扫描的注册。通过将稀疏关键点对应关系整合到密集的连续优化框架中，我们的方法专为大型呼吸运动而设计。关键点对应关系的检测通过共同优化大量潜在的离散位移而实现了抵抗大变形的鲁棒性，而密集连续配准则通过平滑变换实现了体素对齐。这两个步骤均由相同的归一化梯度字段数据项驱动。我们采用曲率正则化和体积变化控制机制来防止变形网格折叠并将Jacobian的行列式限制在生理上有意义的值。通过具有自适应确定的权重的二次惩罚，将关键点对应关系集成到密集注册中。使用无矩阵的并行导数计算方案，在标准PC上实现了大约5分钟的运行时间。所提出的算法在肺图像配准的EMPIRE10挑战中排名第一。此外，它在DIR-Lab COPD数据库上实现了0.82 mm的平均界标距离，从而使现有技术的精度提高了15％。我们的算法是第一个在该数据集的地标注释中达到观察者间差异的算法。

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