  The recent application of deep learning technologies in medical image
registration has exponentially decreased the registration time and gradually
increased registration accuracy when compared to their traditional
counterparts. Most of the learning-based registration approaches considers this
task as a one directional problem. As a result, only correspondence from the
moving image to the target image is considered. However, in some medical
procedures bidirectional registration is required to be performed. Unlike other
learning-based registration, we propose a registration framework with inverse
consistency. The proposed method simultaneously learns forward transformation
and backward transformation in an unsupervised manner. We perform training and
testing of the method on the publicly available LPBA40 MRI dataset and
demonstrate strong performance than baseline registration methods.
