Abstract
In this paper, we propose a self-recursive contextual network for unsupervised 3D medical image registration. Current learning-based registration methods refine an initial deformation field either through cascaded stages or in a coarse-to-fine manner, which improve results at the cost of a rapidly increased number of parameters. Aiming to achieve both elevation of performance and reduction of parameters, we design a novel pyramid structure with a self-recursive scheme and contextual components. Specifically, we adopt a weight-sharing generator to refine the deformation fields recursively across different levels of the pyramid. Meanwhile, we introduce a spatial pyramid pooling module in the feature extractor to capture richer contextual information, as well as a dilated receptive module for the post-processing of the deformation field. Evaluated on two benchmark datasets for 3D medical image registration, our method outperforms the state-of-the-art pyramid network with 39% less parameters, and competes to multi-stage cascaded registration networks with significantly less parameters and faster running speed.
B. Hu and S. Zhou—Contributed equally.
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Acknowledgement
This work was supported in part by Key Area R&D Program of Guangdong Province with grant No. 2018B030338001 and the Fundamental Research Funds for the Central Universities under Grant WK2380000002.
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Hu, B., Zhou, S., Xiong, Z., Wu, F. (2020). Self-recursive Contextual Network for Unsupervised 3D Medical Image Registration. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_7
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