Symmetry and Fusion Data Augmentation for Semi-Supervised Medical Segmentation

Abstract

In semi-supervised medical image segmentation, appropriately merging labeled and unlabeled data before network training instead of using them separately can effectively reduce knowledge loss, mitigate distribution discrepancies and promote efficient knowledge transfer to unlabeled data. However, existing methods tend to focus on data fusion, overlooking the importance of self-transformations. Therefore, we propose a comprehensive data augmentation strategy that combines self-transformations with cross-sample fusion: Symmetry and Fusion Data Augmentation (SF-DA), implemented within the Mean Teacher framework. Our method has two branches: Self-Symmetric Flipping (SSF), enhancing the model’s feature understanding, and cross-sample stitching (CSS), promoting common semantic learning between labeled and unlabeled data. Together, they promote more comprehensive knowledge transfer. Extensive experiments on ACDC and PROMISE12 datasets demonstrate the effectiveness and superiority of SF-DA. Across different labeled data scenarios, SF-DA consistently outperforms the second-best method in all evaluation metrics. Code is available at https://github.com/ZYS-four/SF-DA.git.