  We propose FlowReg, a deep learning-based framework for unsupervised image
registration for neuroimaging applications. The system is composed of two
architectures that are trained sequentially: FlowReg-A which affinely corrects
for gross differences between moving and fixed volumes in 3D followed by
FlowReg-O which performs pixel-wise deformations on a slice-by-slice basis for
fine tuning in 2D. The affine network regresses the 3D affine matrix based on a
correlation loss function that enforces global similarity. The deformable
network operates on 2D image slices based on the optical flow network
FlowNet-Simple but with three loss components. The photometric loss minimizes
pixel intensity differences differences, the smoothness loss encourages similar
magnitudes between neighbouring vectors, and a correlation loss that is used to
maintain the intensity similarity between fixed and moving image slices. The
proposed method is compared to four open source registration techniques ANTs,
Demons, SE, and Voxelmorph. In total, 4643 FLAIR MR imaging volumes are used
from dementia and vascular disease cohorts, acquired from over 60 international
centres with varying acquisition parameters. A battery of quantitative novel
registration validation metrics are proposed that focus on the structural
integrity of tissues, spatial alignment, and intensity similarity. Experimental
results show FlowReg (FlowReg-A+O) performs better than iterative-based
registration algorithms for intensity and spatial alignment metrics with a
Pixelwise Agreement of 0.65, correlation coefficient of 0.80, and Mutual
Information of 0.29. Among the deep learning frameworks, FlowReg-A or
FlowReg-A+O provided the highest performance over all but one of the metrics.
Results show that FlowReg is able to obtain high intensity and spatial
similarity while maintaining the shape and structure of anatomy and pathology.
