• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Conference on Medical Imaging: Computer-Aided Diagnosis >Deep Learning-based Deformable MRI-CBCT Registration of Male Pelvic Region
【24h】

Deep Learning-based Deformable MRI-CBCT Registration of Male Pelvic Region

机译：基于深度学习的可变形MRI-CBCT雄性骨盆区注册

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

摘要

In this study, we propose a novel unsupervised deep learning-based method to register pelvic MRI and CBCT images. No ground truth deformation vector field (DVF) is needed during training. To perform registration between CBCT and MRI, a self-similarity image similarity loss, called as self-correlation descriptor, is used as loss function to learn the trainable parameters in the unsupervised deep neural networks. After training, for a new patient's CBCT and MRI, the deformed MRI is obtained via first feeding the MRI and CBCT into the unsupervised deep neural networks to derive the DVF, then deformed via spatial transformation on MRI and DVF. Our results show that the proposed method has outperformed manual rigid registration. Target registration error calculated between CBCT and deformed MRI is used for es aluation. The average TRE is 2.95±0.94 mm among the 20 prostate patients we retrospectively investigated. The proposed method has great potential in providing accurate image registration and potentially facilitating adaptive radiation therapy by multi-imaging modality.
机译：在这项研究中，我们提出了一种新颖的无监督基于深入学习的方法来注册骨盆MRI和CBCT图像。在训练期间，无需接地真相变形传染媒介领域（DVF）。为了在CBCT和MRI之间进行登记，将自相相似性图像相似性丢失称为自相关描述符，被用作学习无监督的深神经网络中的培训参数的损耗函数。在培训之后，对于新的患者的CBCT和MRI，通过首先将MRI和CBCT进入无监督的深神经网络来获得变形的MRI以导出DVF，然后通过MRI和DVF上的空间转换变形。我们的结果表明，该方法的手动刚性注册表明。 CBCT和变形MRI之间计算的目标登记误差用于ES Aluation。在我们回顾性调查的20个前列腺患者中，平均TRE是2.95±0.94毫米。所提出的方法具有极大的潜力，提供准确的图像配准，并且通过多成像模态提供适应性放射治疗。

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