Abstract
Groupwise image registration (GIR), as a fundamental task during medical image processing, aims to transform a group of images into a common space simultaneously. Most GIR methods consist of creating one template image and registering all group subjects to the template space. This paper presents a novel learning-based template synthesis method that can produce sharper and unbiased template images in shorter runtime. The method is based on the generative adversarial network (GAN) scheme, which is expected to generate authentic images. Besides GAN, we use a registration network to warp images and an auxiliary segmentor to take advantage of semantic information and improve the quality of generated images. First, the label mask will be combined with medical images as the input of the discriminator. Second, we calculate the entropy of pixel-wise label probability distribution as a new loss term. We compare our method with three baseline methods on 2D and 3D brain MR images in the experiments. We evaluate the performance using metrics corresponding to accuracy, smoothness, and unbiasedness. Results illustrate that our method outperforms the baseline methods and can achieve the overall state-of-the-art performance.
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He, Z., Chung, A.C.S. (2021). Learning-Based Template Synthesis for Groupwise Image Registration. In: Svoboda, D., Burgos, N., Wolterink, J.M., Zhao, C. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2021. Lecture Notes in Computer Science(), vol 12965. Springer, Cham. https://doi.org/10.1007/978-3-030-87592-3_6
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