Affine Medical Image Registration with Coarse-to-Fine Vision Transformer 2022CVPR 简单记录 需要解决的问题:3D医疗图像的配准 Motivation: 传统配准和基于CNN的方法通常无法处理好初始大的initial-misalignment和训练集未见场景的情况。且基于CNN的方法通常分为两类, concatenation based and Siamese network approaches,如...
Affine registration is a key component in a wide range of medical image processing frameworks and is extensively applied in clinical settings. Nevertheless, recent works often emphasize the integration of affine and deformable registration, resulting in the standalone performance of affine registration ...
While CNNs have achieved remarkable success in de- formable medical image registration, we argue that CNNs are not an ideal architecture for modelling and learning affine registration. In contrast to deformable image reg- istration, affine registration is often use...
Affine Medical Image Registration with Coarse-to-Fine Vision Transformer Tony C. W. Mok, Albert C. S. Chung CVPR2022.eprint arXiv:2203.15216 Some codes in this repository are modified fromPVTandViT. The MNI152 brain template is provided by theFLIRT (FMRIB's Linear Image Registration Tool)....
几篇论文实现代码:《Affine Medical Image Registration with Coarse-to-Fine Vision Transformer》(CVPR 2022) GitHub: github.com/cwmok/C2FViT [fig9] 《Neural Convolutional Surfaces》(CVPR 2022) GitHub...
SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation Lin Tian*, Zi Li*, Fengze Liu, Xiaoyu Bai, Jia Ge, Le Lu, Marc Niethammer, Xianghua Ye, Ke Yan, Daikai Jin. ArXiv 2023 eprint arXiv:2311.14986 SAME...
Deep learning-based medical image registration can be divided into three main categories, depending on the training procedure: (i) a supervised training [13,14], where a known transformation is applied and being reconstructed, (ii) an unsupervised training [15,16,17], where a given similarity ...
Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2, 1–36 (1998) ArticleGoogle Scholar Roche, A., Malandain, G., Ayache, N.: Unifying Maximum Likelihood Approaches in Medical Image Registration. International Journal of Imaging Systems and Tec...
extensively evaluated, thereby demonstrating the performance gain for a variety of real-life medical applications. Acknowledgments J. Rühaak, L. König, F. Tramnitzke and J. Modersitzki received funding from the European Union, European Regional Development Fund, Grant No. 122-10-002. All ...
By con- sidering not only one affine component, but a mixture of components acting at different scales, we are moving the discussion into structured learning, which to our knowledge is a novelty in the medical registration community. In future work, we plan to introduce a sparse representation...