Medical image segmentation based on cycle consistency data augmentation

Lihua Fu; Junxiang Wang

doi:10.1117/12.2687884

28 August 2023 Medical image segmentation based on cycle consistency data augmentation

Lihua Fu, Junxiang Wang

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.

Citation Download Citation

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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