Enhancing cardiac function assessment: Developing and validating a domain adaptive framework for automating the...

Area of Science:

• Medical Imaging Analysis
• Deep Learning in Cardiology
• Image Segmentation Techniques

Background:

• Accurate echocardiographic image segmentation is crucial for cardiac function assessment, including ejection fraction calculation.
• Deep learning models face challenges in echocardiography due to domain discrepancy, noisy data, and anatomical variability.
• Existing methods struggle with diverse imaging conditions and modalities.

Purpose of the Study:

• To propose and validate a domain adaptive segmentation framework for automated echocardiographic image segmentation.
• To enhance the robustness of segmentation models across varied imaging conditions and modalities.
• To address limitations of current deep learning approaches in cardiac image analysis.

Main Methods:

• Integration of a Variational AutoEncoder (VAE) for structured latent representation and a Wasserstein GAN (WGAN) for domain alignment.
• Utilized depthwise separable convolutions for computational efficiency and PixelShuffle layers for high-resolution reconstruction.
• Evaluated on multiple datasets (CAMUS, EchoNet-Dynamic, Heshmat Hospital) using Dice scores, Jaccard indices, and Hausdorff distances, with qualitative cardiologist assessment.

Main Results:

• Achieved superior Dice scores: 84.6% (CAMUS → EchoNet-Dynamic) and 89.1% (EchoNet-Dynamic → CAMUS), outperforming state-of-the-art Unsupervised Domain Adaptation (UDA) methods.
• Maintained strong performance on an external local dataset (Heshmat Hospital), with Dice scores of 83.0% and 84.1%.
• All results demonstrated statistical significance (p < 0.01) compared to baseline methods.

Conclusions:

• The proposed UDA framework significantly advances echocardiographic segmentation by effectively handling domain discrepancy, noisy data, and anatomical variability.
• This robust framework offers a reliable tool for improved cardiac health assessment.
• The study highlights the potential for automated, accurate segmentation in clinical cardiology.