TY  - EJOU
AU  - Tan, Yun 
AU  - Wu, Weizhao 
AU  - Tan, Ling 
AU  - Peng, Haikuo 
AU  - Qin, Jiaohua 

TI  - Semi-Supervised Medical Image Segmentation Based on Generative Adversarial Network
T2  - Journal of New Media

PY  - 2022
VL  - 4
IS  - 3
SN  - 2579-0129

AB  - 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.
KW  - Medical image; semi-supervised; U-net; generative adversarial network; image segmentation

DO  - 10.32604/jnm.2022.031113