Left ventricle analysis in echocardiographic images using transfer learning.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiology

Background:

• Accurate Left Ventricle (LV) segmentation is crucial for cardiac function assessment.
• 2D echocardiographic images often suffer from poor quality and low contrast, hindering manual delineation.
• An intelligent, automated endocardium segmentation system is needed for clinical efficiency and accuracy.

Purpose of the Study:

• To evaluate deep learning architectures for automated LV segmentation in echocardiography.
• To assess the impact of transfer learning and pre-trained backbones on segmentation performance.
• To compare various U-Net based models including U-Net1, U-Net2, LinkNet, Attention U-Net, and TransUNet.

Main Methods:

• Utilized the public CAMUS dataset for training and validation.
• Employed transfer learning with pre-trained backbones in the encoder of segmentation networks.
• Compared U-Net1, U-Net2, LinkNet, Attention U-Net, and TransUNet architectures.

Main Results:

• The proposed framework achieved a Dice similarity coefficient of 93.30% and a Hausdorff Distance of 4.01 mm.
• Demonstrated good agreement between automatically and manually segmented clinical indices.
• Mean absolute errors for LV volumes and ejection fraction were 7.9 ml, 5.4 ml, and 6.6% respectively.

Conclusions:

• Deep learning, particularly with transfer learning, shows significant promise for automated cardiac image segmentation.
• The developed system offers a reliable and accurate alternative to manual LV segmentation.
• Results encourage further research and potential clinical integration of automated echocardiographic analysis.