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Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

3D medical image registration is of great clinical importance. However, supervised learning methods require a large amount of accurately annotated corresponding control p...Show More

Abstract:

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 article, 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-the-art performance in medical image registration.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 24, Issue: 5, May 2020)
Page(s): 1394 - 1404
Date of Publication: 01 November 2019

ISSN Information:

PubMed ID: 31689224

Funding Agency:


References

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