CT Scan Registration with 3D Dense Motion Field Estimation Using LSGAN

Abstract

This paper reports on a new CT volume registration method, using 3D Convolutional Neural Networks (CNN). The proposed method uses the Least Square Generative Adversarial Network (LSGAN) model consisting of the Contraction-Expansion registration network as the LSGAN’s generator and a deep 3D CNN classification network as the LSGAN’s discriminator. The training of the generator is performed first on its own, using Charbonnier and smoothness loss functions, with progressive weights update moving from lower to higher resolution layers of the Expander. Subsequently, the complete network (Contraction-Expansion with the Discriminator) is trained as a LSGAN network. For the training, CREATIS and COPDgene datasets have been used in a self-supervised paradigm, using 3D warping of the moving volume to estimate the error with respect to the reference volume. The input to the network has 256 × 256 × 128 × 2 voxels and the output is displacement field of 128 × 128 × 64 × 3 voxels. The Contraction-Expansion registration network, on its own, achieves mean error of 1.30 mm with 1.70 standard deviation (SD) on the DIR-LAB dataset. When the whole proposed LSGAN network is used, the mean error is further reduced to 1.13 mm with 0.67 (SD). Therefore, the use of the GAN paradigm reduces the mean error by approximately 15%, providing the state-of-the-art performance.