A Shape-Consistent Deep-Learning Segmentation Architecture for Low-Quality and High-Interference Myocardial Contrast...

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Myocardial contrast echocardiography (MCE) is vital for diagnosing cardiac conditions.
• Accurate MCE image segmentation is crucial for automated analysis of heart diseases.
• Manual segmentation methods in MCE lack repeatability and clinical applicability due to low image quality and noise.

Purpose of the Study:

• To develop a deep-learning network for accurate and consistent MCE image segmentation.
• To address challenges posed by low image quality, noise, and interference in MCE.
• To enhance automated analysis of cardiac conditions using improved MCE segmentation.

Main Methods:

• A deep-learning network incorporating dilated convolutions for high-scale information capture.
• Modified multi-head self-attention mechanisms to improve global context and consistency.
• Cascaded application of transformers with convolutional neural networks for MCE segmentation.

Main Results:

• Achieved a Dice score of 84.35% for standard MCE views, outperforming state-of-the-art models.
• Obtained Dice scores of 83.33% and 83.97% for non-standard views and frames with interfering structures, respectively.
• Demonstrated excellent shape consistency and robustness in segmenting various MCE types.

Conclusions:

• The proposed deep-learning architecture provides precise and consistent myocardial segmentation for MCE.
• Enables fundamental conditions for automated analysis of various heart diseases.
• Has the potential to uncover pathological features and reduce healthcare costs.