Unsupervised Deformable Registration for Multi-modal Images via Disentangled Representations

Qin C, Shi B, Liao R, Mansi T, Rueckert D, Kamen A (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 11492 LNCS

Pages Range: 249-261

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Hong Kong, CHN

ISBN: 9783030203504

DOI: 10.1007/978-3-030-20351-1_19

Abstract

We propose a fully unsupervised multi-modal deformable image registration method (UMDIR), which does not require any ground truth deformation fields or any aligned multi-modal image pairs during training. Multi-modal registration is a key problem in many medical image analysis applications. It is very challenging due to complicated and unknown relationships between different modalities. In this paper, we propose an unsupervised learning approach to reduce the multi-modal registration problem to a mono-modal one through image disentangling. In particular, we decompose images of both modalities into a common latent shape space and separate latent appearance spaces via an unsupervised multi-modal image-to-image translation approach. The proposed registration approach is then built on the factorized latent shape code, with the assumption that the intrinsic shape deformation existing in original image domain is preserved in this latent space. Specifically, two metrics have been proposed for training the proposed network: a latent similarity metric defined in the common shape space and a learning-based image similarity metric based on an adversarial loss. We examined different variations of our proposed approach and compared them with conventional state-of-the-art multi-modal registration methods. Results show that our proposed methods achieve competitive performance against other methods at substantially reduced computation time.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) Siemens AG, Healthcare Sector DE Germany (DE)

How to cite

APA:

Qin, C., Shi, B., Liao, R., Mansi, T., Rueckert, D., & Kamen, A. (2019). Unsupervised Deformable Registration for Multi-modal Images via Disentangled Representations. In James C. Gee, Paul A. Yushkevich, Albert C.S. Chung, Siqi Bao (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 249-261). Hong Kong, CHN: Springer Verlag.

MLA:

Qin, Chen, et al. "Unsupervised Deformable Registration for Multi-modal Images via Disentangled Representations." Proceedings of the 26th International Conference on Information Processing in Medical Imaging, IPMI 2019, Hong Kong, CHN Ed. James C. Gee, Paul A. Yushkevich, Albert C.S. Chung, Siqi Bao, Springer Verlag, 2019. 249-261.

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