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Image synthesis from corrupted contrasts increases the diver- sity of diagnostic information available for many neurological diseases. Recently the image-to-image translation has experienced signi cant lev- els of interest within medical research, beginning with the successful use of the Generative Adversarial Network (GAN) to the introduction of cyclic constraint extended to multiple domains. However, in current ap- proaches, there is no guarantee that the mapping between the two image domains would be unique or one-to-one. In this paper, we introduce a novel approach to unpaired image-to-image translation based on the invertible architecture. The invertible property of the ow-based architecture assures a cycle-consistency of image-to-image translation without additional loss functions. We utilize the temporal informa- tion between consecutive slices to provide more constraints to the optimization for transforming one domain to another in un- paired volumetric medical images. To capture temporal structures in the medical images, we explore the displacement between the consec- utive slices using a deformation eld. In our approach, the deformation eld is used as a guidance to keep the translated slides realistic and con- sistent across the translation. The experimental results have shown that the synthesized images using our proposed approach are able to archive a competitive performance in terms of mean squared error, peak signal- to-noise ratio, and structural similarity index when compared with the existing deep learning-based methods on three standard datasets, i.e. HCP, MRBrainS13 and Brats2019.
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Iterative Image Translation for Unsupervised Domain Adaptation
In this paper, we propose an image-translation-based unsupervised domain adaptation approach that iteratively trains an image translation and a classification network using each other. In Phase A, a classification network is used to guide the image translation to preserve the content and generate images. In Phase B, the generated images are used to train the classification network. With each step, the classification network and generator improve each other to learn the target domain representation. Detailed analysis and the experiments are testimony of the strength of our approach.
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- Award ID(s):
- 1828010
- PAR ID:
- 10344387
- Date Published:
- Journal Name:
- MULL 2021 - Proceedings of the 1st Workshop on Multimedia Understanding with Less Labeling, co-located with ACM MM 2021
- Page Range / eLocation ID:
- 37 to 44
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3476098.3485050
