Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification
Open accessAbstract
Generative adversarial networks (GANs) have been recently applied to medical imaging on different modalities (MRI, CT, X-ray, etc). However there are not many applications on ultrasound modality as a data augmentation technique applied to downstream classification tasks. This study aims to explore and evaluate the generation of synthetic ultrasound fetal brain images via GANs and apply them to improve fetal brain ultrasound plane classification. State of the art GANs stylegan2-ada were applied to fetal brain image generation and GAN-based data augmentation classifiers were compared with baseline classifiers. Our experimental results show that using data generated by both GANs and classical augmentation strategies allows for increasing the accuracy and area under the curve score.
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Two studies focused on deep learning systems for the detection of fetal intracranial planes from 2D ultrasound.
“Two studies focused on deep learning systems for the detection of fetal intracranial planes from 2D ultrasound.92,93”
From Artificial intelligence in obstetric ultrasound: A scoping review. (2023) · cites this paper as [92]
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Cited by
Sorted by citations
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“92,93 developed a system to identify five specific common brain abnormalities including hydrocephalus, ventriculomegaly, Blake's pouch cyst, Dandy Walker malformation and cerebellar vermis hypoplasia.”
BackgroundSendra-Balcells, C., Campello, V. M., Torrents-Barrena, J., Ahmed, Y. A., Elattar, M., Ohene-Botwe, B., Nyangulu, P., Stones, W., Ammar, M., Benamer, L. N., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-29490-3 · Cited by 57 · see citing works
- Authors
- Montero, A., Bonet-Carne, E., & Burgos-Artizzu, X. P.
- Affiliations
- Universitat Oberta de Catalunya (ES)
- Faculty of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
- Corresponding author
- Burgos-Artizzu, Xavier Paolo
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-11-29
- Volume
- 21
- Issue
- 23
- Pages
- 7975
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8659720
- OpenAlex
- W3217791935
- Cited by
- 48
- References
- 27
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 12 · 2023: 10 · 2022: 4
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- 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
- Keywords
- generative adversarial networks, fetal brain ultrasound, synthetic image generation, data augmentation, StyleGAN2-ADA, medical image synthesis, fetal brain imaging
- MeSH terms
- Brain, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Ultrasonography, Neural Networks, Computer
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