Enhancing cardiac function assessment: Developing and validating a domain adaptive framework for automating the segmentation of echocardiogram videos

Background

Accurate segmentation of echocardiographic images is essential for assessing cardiac function, particularly in calculating key metrics such as ejection fraction. However, challenges such as domain discrepancy, noisy data, anatomical variability, and complex imaging conditions often hinder the performance of deep learning models in this domain.

Objective

To propose and validate a domain adaptive segmentation framework for automating the segmentation of echocardiographic images across diverse imaging conditions and modalities.

Method

The framework integrates a Variational AutoEncoder (VAE) for structured latent representation, a Wasserstein GAN (WGAN)-based domain alignment module to reduce feature distribution gaps. These components were selected based on their complementary roles; while the VAE ensures stable reconstruction and domain-invariant encoding, the WGAN aligns source and target feature distributions. It also incorporates depthwise separable convolutions for computational efficiency and employs PixelShuffle layers in the decoder module for high-resolution reconstruction. Experiments were conducted on two publicly available echocardiographic datasets—CAMUS and EchoNet-Dynamic—as well as a newly collected local dataset from Heshmat Hospital, Guilan, Iran, for external evaluation of the model's performance under varying imaging conditions and scanner types. The framework was evaluated using metrics such as Dice scores, Jaccard indices, and Hausdorff distances. A qualitative assessment involving two board-certified cardiologists with extensive experience in echocardiographic interpretation was also conducted to evaluate the clinical relevance and anatomical plausibility of the proposed framework’s segmentation outputs.

Results

The proposed framework achieves Dice scores of 84.6 % (CAMUS → EchoNet-Dynamic) and 89.1 % (EchoNet-Dynamic → CAMUS), outperforming recent state-of-the-art UDA methods. When adapting the Heshmat dataset as the target domain, the model maintains strong performance, achieving 83.0 % (EchoNet-Dynamic → Heshmat) and 84.1 % (CAMUS → Heshmat) Dice scores. All results were statistically significant (p < 0.01) when compared to the top-performing baseline.

Conclusion

By addressing critical challenges in echocardiographic segmentation, the proposed UDA framework could offer a significant advancement in this field. Its ability to handle domain discrepancy, noisy data, and anatomical variability makes it a reliable tool for cardiac health assessment.