Supervised deep learning for semantic segmentation has achieved excellent results in accurately identifying anatomical and pathological structures in medical images. However, it often requires large annotated training datasets, which limits its scalability in clinical settings. To address this challenge, semi-supervised learning is a well-established approach that leverages both labeled and unlabeled data. In this paper, we introduce a novel semi-supervised teacher-student framework for biomedical image segmentation, inspired by the recent success of generative models. Our approach leverages denoising diffusion probabilistic models (DDPMs) to generate segmentation masks by progressively refining noisy inputs conditioned on the corresponding images. The teacher model is first trained in an unsupervised manner using a cycle-consistency constraint based on noise-corrupted image reconstruction, enabling it to generate informative semantic masks. Subsequently, the teacher is integrated into a co-training process with a twin-student network. The student learns from ground-truth labels when available and from teacher-generated pseudo-labels otherwise, while the teacher continuously improves its pseudo-labeling capabilities. Finally, to further enhance performance, we introduce a multi-round pseudo-label generation strategy that iteratively improves the pseudo-labeling process. We evaluate our approach on multiple biomedical imaging benchmarks, spanning multiple imaging modalities and segmentation tasks. Experimental results show that our method consistently outperforms state-of-the-art semi-supervised techniques, highlighting its effectiveness in scenarios with limited annotated data. The code to replicate our experiments can be found at https://github.com/ciampluca/diffusion_semi_supervised_biomedical_image_segmentation

翻译：监督式深度学习在语义分割领域已取得卓越成果，能够精准识别医学图像中的解剖结构与病理特征。然而，该方法通常需要大量标注训练数据，这限制了其在临床环境中的可扩展性。为解决这一挑战，半监督学习作为一种成熟方法，能够同时利用标注与未标注数据。本文受生成模型近期进展的启发，提出一种新颖的用于生物医学图像分割的半监督师生协同训练框架。该方法利用去噪扩散概率模型，通过以对应图像为条件对噪声输入进行渐进式优化来生成分割掩码。教师模型首先基于噪声破坏图像重建的循环一致性约束进行无监督训练，使其能够生成具有信息量的语义掩码。随后，教师模型与孪生学生网络集成至协同训练流程中：学生网络在可获得真实标注时从标注数据学习，否则从教师生成的伪标签学习，而教师模型则持续提升其伪标签生成能力。为进一步提升性能，我们提出多轮伪标签生成策略，通过迭代优化改进伪标签生成过程。我们在涵盖多种成像模态与分割任务的多个生物医学影像基准数据集上评估了所提方法。实验结果表明，该方法在标注数据有限的场景下持续优于当前最先进的半监督技术，凸显了其有效性。复现实验的代码可见于 https://github.com/ciampluca/diffusion_semi_supervised_biomedical_image_segmentation