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The recent application of deep learning technologies in medical imageregistration has exponentially decreased the registration time and graduallyincreased registration accuracy when compared to their traditionalcounterparts. Most of the learning-based registration approaches considers thistask as a one directional problem. As a result, only correspondence from themoving image to the target image is considered. However, in some medicalprocedures bidirectional registration is required to be performed. Unlike otherlearning-based registration, we propose a registration framework with inverseconsistency. The proposed method simultaneously learns forward transformationand backward transformation in an unsupervised manner. We perform training andtesting of the method on the publicly available LPBA40 MRI dataset anddemonstrate strong performance than baseline registration methods.

A Multiple Decoder CNN for Inverse Consistent 3D Image Registration