Non-rigid guided affine image registration in multi-contrast brain MRI using deep networks with stochastic depth

Deep learning based affine registration is a fast and computationally efficient alternative to conventional iterative methods. However, existing solutions are not sensitive to local misalignments. We propose a non-rigid guided affine registration network with stochastic depth which was designed with an affine branch and an optional non-rigid branch. The probability of dropping the non-rigid branch was gradually increased over training epochs. During inference, the non-rigid branch was fully removed, thus making it a pure affine network guided by non-rigid transformations. Model training and quantitative evaluation was performed using a pre-registered multi-contrast brain MRI public dataset.