Abstract
Deformable image registration is a fundamental task in medical imaging. Due to the large computational complexity of deformable registration of volumetric images, conventional iterative methods usually face the tradeoff between the registration accuracy and the computation time in practice. In order to boost the registration performance in both accuracy and runtime, we propose a fast unsupervised convolutional neural network for deformable image registration. Specially, the proposed FDRN possesses a compact encoder-decoder structure and exploits deep supervision, additive forwarding and residual learning. We conducted comparison with the existing state-of-the-art registration methods on the LPBA40 brain MRI dataset. Experimental results demonstrate that our FDRN performs better than the investigated methods qualitatively and quantitatively in Dice score and normalized cross correlation (NCC). Besides, FDRN is a generalized framework for image registration which is not confined to a particular type of medical images or anatomy. It can also be applied to other anatomical structures or CT images.
Abstract (translated)
URL
https://arxiv.org/abs/2011.02307
