@Article{jnm.2022.031113,
AUTHOR = {Yun Tan, Weizhao Wu, Ling Tan, Haikuo Peng, Jiaohua Qin},
TITLE = {Semi-Supervised Medical Image Segmentation Based on Generative Adversarial Network},
JOURNAL = {Journal of New Media},
VOLUME = {4},
YEAR = {2022},
NUMBER = {3},
PAGES = {155--164},
URL = {http://www.techscience.com/JNM/v4n3/48204},
ISSN = {2579-0129},
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.},
DOI = {10.32604/jnm.2022.031113}
}