DeepFLASH: An Efficient Network for Learning-based Medical Image Registration

• URL: http://arxiv.org/abs/2004.02097v1
• Date: Sun, 5 Apr 2020 05:17:07 GMT
• Title: DeepFLASH: An Efficient Network for Learning-based Medical Image Registration
• Authors: Jian Wang, Miaomiao Zhang
• Abstract summary: DeepFLASH is a novel network with efficient training and inference for learning-based medical image registration. We demonstrate our algorithm in two different applications of image registration: 2D synthetic data and 3D real brain magnetic resonance (MR) images.
• Score: 8.781861951759948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents DeepFLASH, a novel network with efficient training and inference for learning-based medical image registration. In contrast to existing approaches that learn spatial transformations from training data in the high dimensional imaging space, we develop a new registration network entirely in a low dimensional bandlimited space. This dramatically reduces the computational cost and memory footprint of an expensive training and inference. To achieve this goal, we first introduce complex-valued operations and representations of neural architectures that provide key components for learning-based registration models. We then construct an explicit loss function of transformation fields fully characterized in a bandlimited space with much fewer parameterizations. Experimental results show that our method is significantly faster than the state-of-the-art deep learning based image registration methods, while producing equally accurate alignment. We demonstrate our algorithm in two different applications of image registration: 2D synthetic data and 3D real brain magnetic resonance (MR) images. Our code is available at https://github.com/jw4hv/deepflash.

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