Cross-modal image segmentation and synthesis in medical imaging

Xiaolong Wu; Zhimin Gan; Jiaxi Xie; Haiyi Lou; Zhengxing Yan; Qiqi Jia; Yingchun Yuan

doi:10.1117/12.2669536

24 March 2023 Cross-modal image segmentation and synthesis in medical imaging

Xiaolong Wu, Zhimin Gan, Jiaxi Xie, Haiyi Lou, Zhengxing Yan, Qiqi Jia, Yingchun Yuan

Proceedings Volume 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022); 126110R (2023) https://doi.org/10.1117/12.2669536
Event: International Conference on Biological Engineering and Medical Science (ICBioMed2022), 2022, Oxford, United Kingdom

Abstract

Medical image is an important type of evidence for diagnosising disease. However, some patients can not receive a complete radiological examination due to the damage of radiation, medical cost or individual differences, so the transmembrane of medical images is essential. Genrative adversarial network (GAN) is an unsupervised deep learning model, which is widely used in the synthesis of medical images. In this research, paired data set which has MRI images of brain is used to compare the ability of transforming T1 of T2 images by pix2pix and cycleGAN. Peak signal to ratio (PSNR) and Mean absolute error (MAE) are used to evaluate the quantity of predicted T2 images. The weight of idenitity loss and hyperpremeters are adjusted to explore a better model of cycleGAN. The conclusion is that cycleGAN with low weight of identity loss is better than that with high weight of identity loss.

Citation Download Citation

Xiaolong Wu, Zhimin Gan, Jiaxi Xie, Haiyi Lou, Zhengxing Yan, Qiqi Jia, and Yingchun Yuan "Cross-modal image segmentation and synthesis in medical imaging", Proc. SPIE 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022), 126110R (24 March 2023); https://doi.org/10.1117/12.2669536

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KEYWORDS

Gallium nitride

Magnetic resonance imaging

Medical imaging

Data modeling

Education and training

Statistical modeling

Adversarial training

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