End to End Unsupervised Rigid Medical Image Registration by Using Convolutional Neural Networks

In this paper, we focus on the issue of rigid medical image registration using deep learning. Under ultrasound, the moving of some organs, e.g., liver and kidney, can be modeled as rigid motion. Therefore, when the ultrasound probe keeps stationary, the registration between frames can be modeled as rigid registration. We propose an unsupervised method with Convolutional Neural Networks. The network estimates from the input image pair the transform parameters first then the moving image is wrapped using the parameters. The loss is calculated between the registered image and the fixed image. Experiments on ultrasound data of kidney and liver verified that the method is capable of achieve higher accuracy compared with traditional methods and is much faster.