BEAS-Net: A Shape-Prior-Based Deep Convolutional Neural Network for Robust Left Ventricular Segmentation in 2-D...

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Left ventricle (LV) segmentation in 2-D echocardiography is crucial for diagnosing cardiovascular diseases (CVD).
• Deep learning (DL) models show promise for automated LV segmentation, but often lack anatomical shape priors.
• This limitation can reduce performance on unseen or low-quality images.

Purpose of the Study:

• To introduce a novel shape-constrained deep convolutional neural network (CNN) for robust automatic LV segmentation.
• To enhance the generalization performance of DL models by incorporating anatomical shape information.

Main Methods:

• Developed BEAS-Net, a CNN that integrates image features with B-spline explicit active surface (BEAS) algorithm-derived shape priors.
• Trained and evaluated BEAS-Net on three in vivo echocardiography datasets.
• Compared BEAS-Net against a U-Net based DL segmentation model.

Main Results:

• BEAS-Net and U-Net showed comparable performance on high-quality echocardiography images.
• BEAS-Net significantly outperformed the U-Net model on artifactual and low-quality images.
• The proposed network generated physiologically meaningful segmentation contours, even with image artifacts.

Conclusions:

• BEAS-Net offers improved robustness and accuracy for LV segmentation, particularly in challenging imaging conditions.
• Integrating anatomical shape priors into DL models enhances their clinical utility for cardiovascular diagnostics.
• This approach holds potential for more reliable automated analysis of cardiac function and morphology.