Tunable image quality control of 3-D ultrasound using switchable CycleGAN
Open accessAbstract
In contrast to 2-D ultrasound (US) for uniaxial plane imaging, a 3-D US imaging system can visualize a volume along three axial planes. This allows for a full view of the anatomy, which is useful for gynecological (GYN) and obstetrical (OB) applications. Unfortunately, the 3-D US has an inherent limitation in resolution compared to the 2-D US. In the case of 3-D US with a 3-D mechanical probe, for example, the image quality is comparable along the beam direction, but significant deterioration in image quality is often observed in the other two axial image planes. To address this, here we propose a novel unsupervised deep learning approach to improve 3-D US image quality. In particular, using unmatched high-quality 2-D US images as a reference, we trained a recently proposed switchable CycleGAN architecture so that every mapping plane in 3-D US can learn the image quality of 2-D US images. Thanks to the switchable architecture, our network can also provide real-time control of image enhancement level based on user preference, which is ideal for a user-centric scanner setup. Extensive experiments with clinical evaluation confirm that our method offers significantly improved image quality as well user-friendly flexibility.
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AI image enhancement methods could produce increased 3D image quality with user-preferential flexibility in both gynecological and obstetric US images.
“AI image enhancement methods could produce increased 3D image quality with user-preferential flexibility in both gynecological and obstetric US images [226].”
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Self-citation
- Authors
- Huh, J., Khan, S., Choi, S., Shin, D., Lee, J. E., Lee, E. S., & Ye, J. C.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Samsung Medison (South Korea) (KR)
- Chungnam National University Hospital (KR)
- Chung-Ang University Hospital (KR)
- Corresponding author
- Lee, Eun Sun
- Ye, Jong Chul
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 83
- Pages
- 102651
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W4226218693
- Cited by
- 14
- References
- 47
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 4 · 2023: 3 · 2022: 2
- FWCI
- 0.98
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- National Research Foundation (NRF-2020R1A2B5B03001980)
- National Research Foundation of Korea (NRF‐2020R1A2B5B03001980)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- 3D ultrasound, image quality control, unsupervised image enhancement, gynecologic ultrasound, real-time image enhancement, user preferences
- MeSH terms
- Humans, Quality Control
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