Integrated Domain Augmentation and Structural Content Reconstruction Based Multisource Domain Generalization on Medical Image Segmentation

Medical image segmentation methods based on deep learning usually suffer from potential domain shift. To address this problem, the domain generalization (DG) method has been studied. However, existing DG medical image segmentation methods based on domain augmentation only enhance the data diversity in input space or feature space individually, which is insufficient to alleviate the model overfitting to source domains. In this article, an effective integrated domain augmentation and structural content reconstruction (IDA-SCR) framework for multisource DG on medical image segmentation is proposed. Specifically, we effectively incorporate image-based and feature-based augmentation methods into an ensemble to make full use of the diversified data to improve the model’s robustness. A Fourier-based full-band amplitude mixing (FAM) module is designed to augment the image style in the input space, and a random feature perturbation (RFP) module is utilized to perturb the extracted features in the feature space. Corresponding to them, two segmentation consistency constraints are leveraged to guide the model to learn domain-invariant knowledge from the augmented data. To further improve the generalization performance, a structural content reconstruction (SCR) self-supervision is proposed. We construct a structural image by the inverse Fourier Transform on the phase spectrum and adopt it instead of the original image as the reconstruction target, which mitigates the effect of the style information contained in the original images and encourages the model to focus on learning semantic structure representations. Our method is validated on three public multi-site medical image datasets. Experimental results show that the proposed IDA-SCR achieves remarkable generalizability across different domains and outperforms several state-of-the-art methods by considerable margins.