Quantum mechanics

Affine transformation

Pure mathematics

Computer science

Image registration

Physics

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Artificial intelligence

Morphing

End-to-end principle

Deep learning

Unsupervised learning

Consistency (knowledge bases)

Convolutional neural network

Volume (thermodynamics)

Computer vision

IEEE Journal of Biomedical and Health Informatics

3D medical image registration is of great clinical importance. However, supervised learning methods require a large amount of accurately annotated corresponding control points (or morphing), which are very difficult to obtain. Unsupervised learning methods ease the burden of manual annotation by exploiting unlabeled data without supervision. In this paper, we propose a new unsupervised learning method using convolutional neural networks under an end-to-end framework, Volume Tweening Network (VTN), for 3D medical image registration. We propose three innovative technical components: (1) An end-to-end cascading scheme that resolves large displacement; (2) An efficient integration of affine registration network; and (3) An additional invertibility loss that encourages backward consistency. Experiments demonstrate that our algorithm is 880x faster (or 3.3x faster without GPU acceleration) than traditional optimization-based methods and achieves state-of-theart performance in medical image registration.

Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network