FDRN: A Fast Deformable Registration Network for Medical Images

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 convolutional neural network. Specially, to efficiently utilize the memory resources and enlarge the model capacity, we adopt additive forwarding instead of channel concatenation and deepen the network in each encoder and decoder stage. To facilitate the learning efficiency, we leverage skip connection within the encoder and decoder stages to enable residual learning and employ an auxiliary loss at the bottom layer with lowest resolution to involve deep supervision. Particularly, the low-resolution auxiliary loss is weighted by an exponentially decayed parameter during the training phase. In conjunction with the main loss in high-resolution grid, we achieve a coarse-to-fine learning strategy. Last but not least, we involve a proposed multi-label segmentation loss to improve the network performance in Dice score. Comparing to average Dice score, the proposed segmentation loss does not require additional memory in the training phase and improves the registration accuracy efficiently. In the experiments, we show FDRN performs better than the existing state-of-the-art registration methods for brain MR images by resorting to the compact autoencoder structure and efficient learning. Besides, FDRN is a generalized framework for image registration which is not confined to a particular type of medical images or anatomy.