Paper
28 August 2023 Medical image segmentation based on cycle consistency data augmentation
Lihua Fu, Junxiang Wang
Author Affiliations +
Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 127241T (2023) https://doi.org/10.1117/12.2687884
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China
Abstract
Traditional deep learning based medical image segmentation methods require a large number of labeled datasets, while medical images lack datasets due to various reasons such as patient privacy, and even more so lack labeled datasets. Therefore, this paper proposes a medical image segmentation method based on cycle consistency data augmentation. First, train space transformation registration network and appearance transformation registration network based on cycle consistency respectively. Then, use the trained space transformation registration network and appearance transformation registration network to perform data enhancement on the original dataset. Finally, the augmented data is used to train segmentation network to achieve medical image segmentation. Experiments on public datasets show that this method has better segmentation effect and better image registration effect.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lihua Fu and Junxiang Wang "Medical image segmentation based on cycle consistency data augmentation", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 127241T (28 August 2023); https://doi.org/10.1117/12.2687884
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KEYWORDS
Image registration

Image segmentation

Medical imaging

Education and training

Image enhancement

Image quality

Deep learning

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