---

Medical image registration is a typical two-image task which requires specialized feature representation networks for deep-learning-based methods (The existing methods and their limitations have been evaluated in our papers). Therefore, we designed a X-shape feature representation backbone which combines the relationship-aware capacity of Transformer and the traits of two-image tasks which foucus not only on structure information of each image but also on cross correspondence between the image pair. The overall structure of our network is following:

Paper

This repository provides the official implementation of XMorpher and its application under two different strategies in the following paper: XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention
Jiacheng Shi¹, Yuting He¹, Youyong Kong^1,2,3,
Jean-Louis Coatrieux^1,2,3, Huazhong Shu^1,2,3, Guanyu Yang^1,2,3, and Shuo Li^4
1 LIST, Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China
² Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing
³ Centre de Recherche en Information Biomédicale Sino-Français (CRIBs)
⁴ Dept. of Medical Biophysics, University of Western Ontario, London, ON, Canada

International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022
paper | code | poster | video

Citation

If you use this code or use our pre-trained weights for your research, please cite our papers:

@inproceedings{shi2022xmorpher,
  title={XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention},
  author={Shi, Jiacheng and He, Yuting and Kong, Youyong and Coatrieux, Jean-Louis and Shu, Huazhong and Yang, Guanyu and Li, Shuo},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={217--226},
  year={2022},
  organization={Springer}
}

Available implementation

• MindSpore/ (updating)
• Pytorch/

★ Notes: implemented under two training strategies VoxelMorph and PC-Reg and the detailed corresponding main functions are Unsup-train.py and Semi-train.py respectively (Pytorch)

Major results from our work

1. XMorpher has the best DSC score and Jacobian score under both strategies

2. XMorpher has visual superiority on some detailed structures

Acknowledgement

This work was supported in part by the National Natural Science Foundation under grants (62171125, 61828101), CAAI-Huawei MindSpore Open Fund, CANN(Compute Architecture for Neural Networks), Ascend AI Processor, and Big Data Computing Center of Southeast University.