论文标题
无监督的可变形图像登记，术前和术后脑肿瘤MRI扫描中缺乏对应关系
Unsupervised Deformable Image Registration with Absent Correspondences in Pre-operative and Post-Recurrence Brain Tumor MRI Scans
论文作者
论文摘要
通常需要对术前和术后大脑图像进行注册，以评估脑神经胶质瘤治疗的有效性。尽管最近基于深度学习的可变形登记方法在健康的大脑图像方面取得了显着的成功，但由于参考图像中缺乏对应关系，它们中的大多数人将无法与病理相处。在本文中，我们提出了一种基于深度学习的可变形登记方法，该方法共同估计缺乏对应关系和双向变形场的区域。前向后的一致性约束用于帮助从两个图像中缺乏对应关系的体素的切除和复发区域的定位。来自Brats-Reg挑战的3D临床数据的结果表明，与传统和深度学习的注册方法相比，我们的方法可以改善图像对齐方式，无论有或没有成本功能掩盖策略。源代码可从https://github.com/cwmok/dirac获得。
Registration of pre-operative and post-recurrence brain images is often needed to evaluate the effectiveness of brain gliomas treatment. While recent deep learning-based deformable registration methods have achieved remarkable success with healthy brain images, most of them would be unable to accurately align images with pathologies due to the absent correspondences in the reference image. In this paper, we propose a deep learning-based deformable registration method that jointly estimates regions with absent correspondence and bidirectional deformation fields. A forward-backward consistency constraint is used to aid in the localization of the resection and recurrence region from voxels with absence correspondences in the two images. Results on 3D clinical data from the BraTS-Reg challenge demonstrate our method can improve image alignment compared to traditional and deep learning-based registration approaches with or without cost function masking strategy. The source code is available at https://github.com/cwmok/DIRAC.
