Deformable image registration, estimating the spatial transformation between different images, is an important task in medical imaging. Many previous studies have used learning-based methods for multi-stage registration to perform 3D image registration to improve performance. The performance of the multi-stage approach, however, is limited by the size of the receptive field where complex motion does not occur at a single spatial scale. We propose a new registration network combining recursive network architecture and mutual attention mechanism to overcome these limitations. Compared with the state-of-the-art deep learning methods, our network based on the recursive structure achieves the highest accuracy in lung Computed Tomography (CT) data set (Dice score of 92\% and average surface distance of 3.8mm for lungs) and one of the most accurate results in abdominal CT data set with 9 organs of various sizes (Dice score of 55\% and average surface distance of 7.8mm). We also showed that adding 3 recursive networks is sufficient to achieve the state-of-the-art results without a significant increase in the inference time.
@article{arxiv.2206.01863,
title = {Recursive Deformable Image Registration Network with Mutual Attention},
author = {Jian-Qing Zheng and Ziyang Wang and Baoru Huang and Ngee Han Lim and Tonia Vincent and Bartlomiej W. Papiez},
journal= {arXiv preprint arXiv:2206.01863},
year = {2024}
}
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arXiv admin note: text overlap with arXiv:2203.04290