华中科技大学学报（自然科学版）2024，Vol.52Issue(5)：70-75,6.DOI:10.13245/j.hust.240478
基于对比学习的半监督肝脏血管分割方法
Semi-supervised liver vessel segmentation method based on contrastive learning
摘要
Abstract
Aiming at the problem that existing methods often depend on high-quality labeled data for model training,with a tendency to focus excessively on local information while neglecting global information,a semi-supervised liver vessel segmentation method was proposed based on global and local contrast learning.First,the Mean Teacher model was used as a framework to globally contrast the high-dimensional features output by the encoder,the global consistency of the features was captured,and richer global context information was obtained.Then,local contrast on the decoder output was performed to obtain the local pixel level in semantic segmentation features.Finally,the distribution difference minimization method was introduced to reduce the distribution difference between labeled and unlabeled data using the discriminator for adversarial training,which improved the generalization performance of the model.Experiment results show that the proposed method achieves remarkable results in the liver vessel segmentation task,with Dice of 74.36%,Jaccard of 59.73%,average surface distance(ASD)of 2.65 mm,and 95%Hausdorff distance(95HD)of 13.57 mm on the 3Dircadb dataset,which is superior to other semi-supervised methods.关键词
肝脏血管分割/半监督学习/深度学习/对比学习/对抗学习Key words
liver vessel segmentation/semi-supervised learning/deep learning/contrastive learning/adversarial learning分类
信息技术与安全科学引用本文复制引用
刘哲,胡芮,宋余庆,刘毅..基于对比学习的半监督肝脏血管分割方法[J].华中科技大学学报（自然科学版）,2024,52(5):70-75,6.基金项目
国家自然科学基金资助项目(61976106,62276116) （61976106,62276116）
江苏省六大人才高峰计划资助项目(DZXX$-$122). （DZXX$-$122）
