Dual-consistency guidance semi-supervised medical image segmentation with low-level detail feature augmentation

被引：0
|
作者：
Wang, Bing [1 ,2 ]
Ju, Mengyi [1 ]
Zhang, Xin [3 ]
Yang, Ying [4 ]
Tian, Xuedong [5 ]
机构：
[1] College of Mathematics and Information Science, Hebei University, Wusi Road 180, Hebei, Baoding,071000, China
[2] Hebei Key Laboratory of Machine Learning and Computational Intelligence, Hebei University, Wusi Road 180, Hebei, Baoding,071000, China
[3] College of Electronic Information Engineering, Hebei University, Qiyi Road 2666, Hebei, Baoding,071000, China
[4] Hebei University Affiliated Hospital, Hebei University, Wusi Road 180, Hebei, Baoding,071000, China
[5] College of Cyber Security and Computer, Hebei University, Wusi Road 180, Hebei, Baoding,071000, China
来源：
Computers in Biology and Medicine | 2024年 / 181卷
关键词：
In deep-learning-based medical image segmentation tasks; semi-supervised learning can greatly reduce the dependence of the model on labeled data. However; existing semi-supervised medical image segmentation methods face the challenges of object boundary ambiguity and a small amount of available data; which limit the application of segmentation models in clinical practice. To solve these problems; we propose a novel semi-supervised medical image segmentation network based on dual-consistency guidance; which can extract reliable semantic information from unlabeled data over a large spatial and dimensional range in a simple and effective manner. This serves to improve the contribution of unlabeled data to the model accuracy. Specifically; we construct a split weak and strong consistency constraint strategy to capture data-level and feature-level consistencies from unlabeled data to improve the learning efficiency of the model. Furthermore; we design a simple multi-scale low-level detail feature enhancement module to improve the extraction of low-level detail contextual information; which is crucial to accurately locate object contours and avoid omitting small objects in semi-supervised medical image dense prediction tasks. Quantitative and qualitative evaluations on six challenging datasets demonstrate that our model outperforms other semi-supervised segmentation models in terms of segmentation accuracy and presents advantages in terms of generalizability. Code is available at https://github.com/0Jmyy0/SSMIS-DC. © 2024 Elsevier Ltd;
D O I：
10.1016/j.compbiomed.2024.109046
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