Cross-Modality Image Synthesis from Unpaired Data Using CycleGAN: Effects of Gradient Consistency Loss and Training Data Size
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Performing MR-to-CT synthesis through the naive CycleGAN often results in inconsistent structures between input and synthetic images.
“However, performing MR-to-CT synthesis through the naive CycleGAN often results in inconsistent structures between input and synthetic images.”
From Towards 2D/3D Registration of the Preoperative MRI to Intraoperative Fluoroscopic Images for Visualisation of Bone Defects (2023) · cites this paper as Hiasa et al. 2018
Additional loss functions such as gradient consistency loss and structure-consistency loss have shown promising results for avoiding geometric distortions in MR-to-CT synthesis.
“To avoid geometric distortions, additional loss functions such as gradient consistency loss (Hiasa et al. 2018) and structure-consistency loss (Yang et al. 2020) have shown promising results.”
From Towards 2D/3D Registration of the Preoperative MRI to Intraoperative Fluoroscopic Images for Visualisation of Bone Defects (2023) · cites this paper as Hiasa et al. 2018
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References 17 · Cited by 221
- Authors
- Hiasa, Y., Otake, Y., Takao, M., Matsuoka, T., Takashima, K., Carass, A., Prince, J. L., Sugano, N., & Sato, Y.
- Affiliations
- Nara Institute of Science and Technology (JP)
- The University of Osaka (JP)
- Johns Hopkins University (US)
- Corresponding author
- Hiasa, Yuta
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 31-41
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2962784654
- Cited by
- 221
- References
- 17
- Citations by year
- 2026: 14 · 2025: 20 · 2024: 20 · 2023: 46 · 2022: 32 · 2021: 35 · 2020: 22 · 2019: 27 · 2018: 5
- FWCI
- 6.70
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- cross-modality image synthesis, CycleGAN, unpaired data, pelvic imaging, segmentation accuracy, training data size, osteonecrosis diagnosis
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