1 research outputs found
On Reducing Negative Jacobian Determinant of the Deformation Predicted by Deep Registration Networks
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 approach 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 have used a local smoothness constraint (typically, a spatial
variation loss) to address the smoothness issue. These networks usually produce
non-invertible transformations with ``folding'' in multiple voxel locations,
indicated by a negative determinant of the Jacobian matrix of the
transformation. While using a loss function that specifically penalizes the
folding is a straightforward solution, this usually requires carefully tuning
the regularization strength, especially when there are also other losses. In
this paper we address this problem from a different angle, by investigating
possible training mechanisms that will help the network avoid negative
Jacobians and produce smoother deformations. We contribute two independent
ideas in this direction. Both ideas greatly reduce the number of folding
locations in the predicted deformation, without making changes to the
hyperparameters or the architecture used in the existing baseline registration
network