吉林大学学报（信息科学版）2018，Vol.36Issue(2)：158-164,7.
基于半监督阶梯网络的肝脏CT影像分割
Liver Segmentation in CT Image Based on Semi-Supervised Ladder Network
摘要
Abstract
Aiming at the challenges, such as fewer labeled samples and expensive manual annotation in medical images, a network of liver CT ( Computed Tomography ) images segmentation model based on semi-supervised ladder is presented. First, the input data is reduced by super-pixel segmentation. Next, the patches are extracted around the center of pixels, and the patches are used to train a semi-supervised model. Finally, the trained model is used to achieve liver segmentation. Experiment results show that a small number of labeled pictures are able to obtain similar results with supervised learning.关键词
半监督学习/阶梯网络/医学图像分割/超像素Key words
semi supervised learning/ladder network/medical image segmentation/super-pixel分类
信息技术与安全科学引用本文复制引用
金兰依,郭树旭,马树志,刘晓鸣,孙长建,李雪妍..基于半监督阶梯网络的肝脏CT影像分割[J].吉林大学学报（信息科学版）,2018,36(2):158-164,7.基金项目
吉林省自然科学基金学科布局基金资助项目(20180101039JC) （20180101039JC）
