Registration of 3D medical images based on unsupervised cooperative cascade of deep networks.

In this paper, a deformable registration network (DR-Net) and a multi-scale cascading strategy are designed for the registration of largely deformed 3D medical images. Our DR-Net appears as a U-shaped convolutional neural network with a pyramidal input module (PIM), a light weighted sequential Inception module and an SCAM convolutional attention module. Our multi-scale cooperative cascading strategy integrates the deformation field information within and between sub-networks at different scales to synthesize the cascaded deformation fields. To cooperatively train the cascaded network, not only the output of the final network layer but also the multi-scale outputs from different layers of the decoder in the last cascaded sub-network are used to calculate loss function. As compared with the VoxelMorph and IVTN, the average dice similarity coefficients (Dice) achieved with our DR-Net are 2.4% and 2.5% higher on the Sliver dataset and are 2.5% and 2.4% higher on the LiTS dataset. The average Dice coefficients achieved with our multi-scale cascading strategy of three DR-Nets are 1.6% and 1.9% higher than those of the VM-CR3 and are 1.5% and 1.7% higher than those of the IVTN-CR3 on these two datasets, respectively. These results show that not only our proposed DR-Net itself but also the cascade of them outperform the state-of-the-art methods and their cascades in registration accuracy. • A novel lightweight deformable registration network (DR-Net) with an SCAM convolutional attention module is proposed. • A multi-scale cascading strategy for synthesizing the cascaded deformation fields is proposed. • A cooperative training strategy is proposed to train the cascaded network. • Extensive experiments on large-scale public datasets from different medical center are conducted. [ABSTRACT FROM AUTHOR]