Deform-GAN:An Unsupervised Learning Model for Deformable Registration
This addresses the challenging problem of aligning medical images across sequences and modalities for researchers and clinicians, though it appears incremental as it builds on existing deep-learning-based registration approaches.
The paper tackles deformable registration for 3D medical images by proposing an unsupervised learning model that introduces a gradient loss and adversarial learning, achieving improved accuracy and speed compared to other methods.
Deformable registration is one of the most challenging task in the field of medical image analysis, especially for the alignment between different sequences and modalities. In this paper, a non-rigid registration method is proposed for 3D medical images leveraging unsupervised learning. To the best of our knowledge, this is the first attempt to introduce gradient loss into deep-learning-based registration. The proposed gradient loss is robust across sequences and modals for large deformation. Besides, adversarial learning approach is used to transfer multi-modal similarity to mono-modal similarity and improve the precision. Neither ground-truth nor manual labeling is required during training. We evaluated our network on a 3D brain registration task comprehensively. The experiments demonstrate that the proposed method can cope with the data which has non-functional intensity relations, noise and blur. Our approach outperforms other methods especially in accuracy and speed.