Enhancing Left Ventricular Segmentation in Echocardiograms Through GAN-Based Synthetic Data Augmentation and...

Area of Science:

• Medical Imaging
• Artificial Intelligence in Medicine
• Cardiovascular Diagnostics

Background:

• Accurate left ventricular segmentation in echocardiograms is vital for cardiovascular disease diagnosis and monitoring.
• Limited high-quality annotated datasets and image complexities hinder traditional segmentation methods.
• Existing techniques often lack generalizability across diverse echocardiogram qualities.

Purpose of the Study:

• To develop a robust framework for enhanced left ventricular segmentation in echocardiograms.
• To integrate Generative Adversarial Networks (GANs) for synthetic data augmentation.
• To improve segmentation accuracy and reliability using a MultiResUNet architecture.

Main Methods:

• A GAN-based framework was developed to generate synthetic echocardiogram images and segmentation masks.
• Synthetic and real echocardiogram data (EchoNet-Dynamic) were used to train a MultiResUNet model.
• The MultiResUNet incorporated multi-resolution blocks, residual connections, attention mechanisms, ASPP, and SELUs.

Main Results:

• The proposed method achieved a Dice Similarity Coefficient of 95.68% and an Intersection over Union (IoU) of 91.62%.
• This represents a significant improvement over existing methods, with a 2.58% increase in Dice and 4.84% in IoU.
• GAN-based augmentation effectively addressed data scarcity and enhanced segmentation performance.

Conclusions:

• The combined use of GAN-generated data and MultiResUNet offers a powerful solution for left ventricular segmentation.
• This approach improves the accuracy of automated diagnostic tools for cardiovascular medicine.
• The framework shows potential for enhancing clinical decision-making, especially with limited or complex data.