Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing data utilization efficiency. Previous methods primarily focus on complex training strategies to utilize unlabeled data but neglect the importance of graph structural information. Different from existing methods, we propose a graph-based clustering for semi-supervised medical image segmentation (GraphCL) by jointly modeling graph data structure in a unified deep model. The proposed GraphCL model enjoys several advantages. Firstly, to the best of our knowledge, this is the first work to model the data structure information for semi-supervised medical image segmentation (SSMIS). Secondly, to get the clustered features across different graphs, we integrate both pairwise affinities between local image features and raw features as inputs. Extensive experimental results on three standard benchmarks show that the proposed GraphCL algorithm outperforms state-of-the-art semi-supervised medical image segmentation methods.

翻译：半监督学习（SSL）在医学图像分割（MIS）领域取得了显著进展，尤其在标注数据有限的情况下，显著提升了数据利用效率。现有方法主要侧重于利用未标注数据的复杂训练策略，但忽略了图结构信息的重要性。与现有方法不同，我们提出了一种基于图聚类的半监督医学图像分割方法（GraphCL），通过在统一的深度模型中联合建模图数据结构。所提出的GraphCL模型具有多重优势。首先，据我们所知，这是首个为半监督医学图像分割（SSMIS）建模数据结构信息的工作。其次，为获取跨不同图的聚类特征，我们将局部图像特征间的成对亲和度与原始特征共同作为输入。在三个标准基准数据集上的大量实验结果表明，所提出的GraphCL算法优于当前最先进的半监督医学图像分割方法。