Cycle-consistent training for reducing negative jacobian determinant in deep registration networks

Abstract

Image registration is a fundamental step in medical image analysis. Ideally, the transformation that registers one image to another should be a diffeomorphism that is both invertible and smooth. Traditional methods like geodesic shooting study the problem via differential geometry, with theoretical guarantees that the resulting transformation will be smooth and invertible. Most previous research using unsupervised deep neural networks for registration address the smoothness issue directly either by using a local smoothness constraint (typically, a spatial variation loss), or by designing network architectures enhancing spatial smoothness. In this paper, we examine this problem from a different angle by investigating possible training mechanisms/tasks that will help the network avoid predicting transformations with negative Jacobians and produce smoother deformations. The proposed cycle consistent idea reduces the number of folding locations in predicted deformations without making changes to the hyperparameters or the architecture used in the existing backbone registration network. Code for the paper is available at https://github.com/dykuang/Medical-image-registration.