Abstract
As a basic building block in medical image analysis, image registration has been greatly developed since the emergence of modern deep neural networks. Compared to non-learning-based methods, the latest approaches can learn task-specific features spontaneously, thus generate the registration results with one round of inference. However, when large inter-image distortion occurs, the stability of existing methods can be strongly affected. To alleviate this problem, the iterative framework based on coarse-to-fine strategies has been introduced in recent works. However, their networks at each iteration step are relatively independent, which is not an optimal solution for the reinforcement of image features. What is more, the moving and the fixed images are often concatenated or fed to identical network layers. Consequently, the iterative learning and warping on the moving image can be entangled with the fixed image. In order to address these issues, we present a novel medical image registration framework, namely ULAE-net, to continuously enhance the spatial transformation and establish more profound contextual dependencies under a compact network layout. Extensive experiments on 3D brain MRI data sets demonstrate that our method has greatly improved the registration performance, thereby outperforms state-of-the-art methods under large-scale deformations (https://github.com/wanghaostu/ULAE-net).
This research was funded in part by the National Natural Science Foundation of China 61906024, 61976031, and 61801068, National Major Scientific Research Instrument Development Project of China 62027827, National Key R&D Program of China 2019YFE0110800, 2016YFC1000307-3.
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Shu, Y., Wang, H., Xiao, B., Bi, X., Li, W. (2021). Medical Image Registration Based on Uncoupled Learning and Accumulative Enhancement. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12904. Springer, Cham. https://doi.org/10.1007/978-3-030-87202-1_1
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