  Deep learning-based medical image registration and segmentation joint models
utilize the complementarity (augmentation data or weakly supervised data from
registration, region constraints from segmentation) to bring mutual improvement
in complex scene and few-shot situation. However, further adoption of the joint
models are hindered: 1) the diversity of augmentation data is reduced limiting
the further enhancement of segmentation, 2) misaligned regions in weakly
supervised data disturb the training process, 3) lack of label-based region
constraints in few-shot situation limits the registration performance. We
propose a novel Deep Complementary Joint Model (DeepRS) for complex scene
registration and few-shot segmentation. We embed a perturbation factor in the
registration to increase the activity of deformation thus maintaining the
augmentation data diversity. We take a pixel-wise discriminator to extract
alignment confidence maps which highlight aligned regions in weakly supervised
data so the misaligned regions' disturbance will be suppressed via weighting.
The outputs from segmentation model are utilized to implement deep-based region
constraints thus relieving the label requirements and bringing fine
registration. Extensive experiments on the CT dataset of MM-WHS 2017 Challenge
show great advantages of our DeepRS that outperforms the existing
state-of-the-art models.
