Li Lei, Xue Miaosen, Li Songyang, Dong Zhuoli, Liao Tianli, Li Peng
Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Zhengzhou, 450001, China.
College of Information Science and Engineering (Henan University of Technology), Zhengzhou, 450001, China.
Interdiscip Sci. 2025 Jul 11. doi: 10.1007/s12539-025-00727-1.
Semi-supervised medical image segmentation techniques have demonstrated significant potential and effectiveness in clinical diagnosis. The prevailing approaches using the mean-teacher (MT) framework achieve promising image segmentation results. However, due to the unreliability of the pseudo labels generated by the teacher model, existing methods still have some inherent limitations that must be considered and addressed. In this paper, we propose an innovative semi-supervised method for medical image segmentation by combining the heterogeneous complementary correction network and confidence contrastive learning (HC-CCL). Specifically, we develop a triple-branch framework by integrating a heterogeneous complementary correction (HCC) network into the MT framework. HCC serves as an auxiliary branch that corrects prediction errors in the student model and provides complementary information. To improve the capacity for feature learning in our proposed model, we introduce a confidence contrastive learning (CCL) approach with a novel sampling strategy. Furthermore, we develop a momentum style transfer (MST) method to narrow the gap between labeled and unlabeled data distributions. In addition, we introduce a Cutout-style augmentation for unsupervised learning to enhance performance. Three medical image datasets (including left atrial (LA) dataset, NIH pancreas dataset, Brats-2019 dataset) were employed to rigorously evaluate HC-CCL. Quantitative results demonstrate significant performance advantages over existing approaches, achieving state-of-the-art performance across all metrics. The implementation will be released at https://github.com/xxmmss/HC-CCL .
半监督医学图像分割技术在临床诊断中已展现出巨大潜力和有效性。使用均值教师（MT）框架的主流方法取得了不错的图像分割结果。然而，由于教师模型生成的伪标签不可靠，现有方法仍存在一些固有局限性，必须加以考虑和解决。在本文中，我们提出了一种创新的半监督医学图像分割方法，即结合异构互补校正网络和置信度对比学习（HC - CCL）。具体而言，我们通过将异构互补校正（HCC）网络集成到MT框架中，开发了一个三分支框架。HCC作为一个辅助分支，用于校正学生模型中的预测误差并提供互补信息。为了提高我们提出模型的特征学习能力，我们引入了一种具有新颖采样策略的置信度对比学习（CCL）方法。此外，我们开发了一种动量风格迁移（MST）方法来缩小标记数据和未标记数据分布之间的差距。另外，我们为无监督学习引入了一种Cutout风格的数据增强方法以提高性能。我们使用了三个医学图像数据集（包括左心房（LA）数据集、美国国立卫生研究院胰腺数据集、Brats - 2019数据集）对HC - CCL进行了严格评估。定量结果表明，与现有方法相比，HC - CCL具有显著的性能优势，在所有指标上均达到了当前最优性能。该实现将在https://github.com/xxmmss/HC-CCL上发布。
