Uncertainty-Aware Meta-weighted Optimization Framework for Domain-Generalized Medical Image Segmentation

Accurate segmentation of echocardiograph images is essential for the diagnosis of cardiovascular diseases. Recent advances in deep learning have opened a possibility for automated cardiac image segmentation. However, the data-driven echocardiography segmentation schemes suffer from domain shift problems, since the ultrasonic image characteristics are largely affected by measurement conditions determined by device and probe specification. In order to overcome this problem, we propose a domain generalization method, utilizing a generative model for data augmentation. An acoustic content- and style-aware diffusion probabilistic model is proposed to synthesize echocardiography images of diverse cardiac anatomy and measurement conditions. In addition, a meta-learning-based spatial weighting scheme is introduced to prevent the network from training unreliable pixels of synthetic images, thereby achieving precise image segmentation. The proposed framework is thoroughly evaluated using both in-distribution and out-of-distribution echocardiography datasets and demonstrates outstanding performance compared to state-of-the-art methods. Code is available at https://github.com/Seokhwan-Oh/MLSW .