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- W4283770105 abstract "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 3.8 mm 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 7.8 mm). 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." @default.
- W4283770105 created "2022-07-03" @default.
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- W4283770105 date "2022-01-01" @default.
- W4283770105 modified "2023-09-28" @default.
- W4283770105 title "Recursive Deformable Image Registration Network with Mutual Attention" @default.
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- W4283770105 doi "https://doi.org/10.1007/978-3-031-12053-4_6" @default.
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