A Semi-Supervised Approach Combining Image and Frequency Enhancement for Echocardiography Segmentation

Abstract

Segmentation of internal structures in echocardiography enables quantitative measurements of the cardiac size, shape, and function, thereby facilitating automated diagnosis and treatment planning. Semi-supervised learning alleviates the demand for large amounts of annotations in deep learning. The existing interpolation-based data augmentation (perturbation) methods demonstrate significant potential, yet they solely focus on operating pixels in the spatial domain, overlooking considerations in the frequency domain. Our proposed interpolation method at the frequency domain level operates on a level where the spatial arrangement of pixels is less relevant. It is capable of generating potent and diverse perturbations while concurrently circumventing disruption to the spatial structure of the image, thereby augmenting the robustness of semi-supervised frameworks. Specifically, we transform annotated and unannotated images into the frequency domain separately. Then, half of the elements in each frequency domain map are randomly selected and mixed with each other. Finally, the resulting mixed frequency domain images are transformed back into the spatial domain to generate mixed images. Furthermore, we refine the spatial domain CutMix method by devising a cutting strategy that adheres to jigsaw partitioning rules, facilitating the model’s perception of local cardiac anatomical structures. Extensive experiments on two public echocardiography segmentation datasets demonstrate the effectiveness of the proposed method.

Keywords

• Semi-supervised learning
• echocardiography
• semantic segmentation
• frequency domain