Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence
Motivation: To improve the performance of deep learning based right ventricle (RV) segmentation in cine cardiac MRI, especially for images with low image quality.
Goal(s): To develop an efficient and accurate RV segmentation by UNet++ in combination with different encoders.
Approach: The UNet++ model was combined with encoders of EfficientNet-b5, EfficientNet-b4, and ResNet50, and optimized with a combined Dice Loss and BCEWithLogitsLoss.
Results: The model achieved a high DSC in both validation and testing sets, outperforming traditional UNet methods, especially in images with low-quality.
Impact: The UNet++ model combined with special encoders provided a robust solution to RV segmentation, with the potential to enhance cardiac imaging analysis and support clinical decision-making by reducing manual intervention and increasing accuracy in heart function assessment.
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