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Semi-Supervised Medical Image Segmentation Based on Generative Adversarial Network

Yun Tan1,2, Weizhao Wu2, Ling Tan3, Haikuo Peng2, Jiaohua Qin2,*

1 Hunan Applied Technology University, Changde, 415000, China
2 Central South University of Forestry and Technology, Changsha, 410004, China
3 The Second Xiangya Hospital of Central South University, Changsha, 410011, China

* Corresponding Author: Jiaohua Qin. Email: email

Journal of New Media 2022, 4(3), 155-164. https://doi.org/10.32604/jnm.2022.031113

Abstract

At present, segmentation for medical image is mainly based on fully supervised model training, which consumes a lot of time and labor for dataset labeling. To address this issue, we propose a semi-supervised medical image segmentation model based on a generative adversarial network framework for automated segmentation of arteries. The network is mainly composed of two parts: a segmentation network for medical image segmentation and a discriminant network for evaluating segmentation results. In the initial stage of network training, a fully supervised training method is adopted to make the segmentation network and the discrimination network have certain segmentation and discrimination capabilities. Then a semi-supervised method is adopted to train the model, in which the discriminant network will generate pseudo-labels on the results of the segmentation for semi-supervised training of the segmentation network. The proposed method can use a small part of annotated dataset to realize the segmentation of medical images and effectively solve the problem of insufficient medical image annotation data.

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Cite This Article

Y. Tan, W. Wu, L. Tan, H. Peng and J. Qin, "Semi-supervised medical image segmentation based on generative adversarial network," Journal of New Media, vol. 4, no.3, pp. 155–164, 2022.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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