  This study investigates the use of the unsupervised deep learning framework
VoxelMorph for deformable registration of longitudinal abdominopelvic CT images
acquired in patients with bone metastases from breast cancer. The CT images
were refined prior to registration by automatically removing the CT table and
all other extra-corporeal components. To improve the learning capabilities of
VoxelMorph when only a limited amount of training data is available, a novel
incremental training strategy is proposed based on simulated deformations of
consecutive CT images. In a 4-fold cross-validation scheme, the incremental
training strategy achieved significantly better registration performance
compared to training on a single volume. Although our deformable image
registration method did not outperform iterative registration using NiftyReg
(considered as a benchmark) in terms of registration quality, the registrations
were approximately 300 times faster. This study showed the feasibility of deep
learning based deformable registration of longitudinal abdominopelvic CT images
via a novel incremental training strategy based on simulated deformations.
