EchoSegDiff: a diffusion-based model for left ventricular segmentation in echocardiography.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiology

Background:

• Accurate left ventricular segmentation is crucial for echocardiography-based cardiac diagnosis.
• Existing methods may face challenges in precise delineation for clinical decision-making.

Purpose of the Study:

• To introduce EchoSegDiff, a new deep learning model for automated left ventricular segmentation in echocardiography.
• To evaluate the performance of EchoSegDiff against state-of-the-art networks.

Main Methods:

• Developed EchoSegDiff, utilizing an encoder-decoder structure within a reverse diffusion process.
• Incorporated Diffusion Encoder Residual Blocks (DEResblocks) with Atrous Pyramid Squeeze Attention (APSA) for multiscale feature extraction.
• Introduced a Feature Fusion Module (FFM) to adaptively merge encoder-decoder features, minimizing semantic gaps.

Main Results:

• EchoSegDiff achieved high segmentation accuracy on two public echocardiography datasets, reaching 93.69% and 89.95%.
• The model outperformed existing state-of-the-art networks in left ventricular segmentation tasks.
• Demonstrated effective capture of multiscale features and reduced semantic discrepancies.

Conclusions:

• EchoSegDiff shows significant potential for accurate left ventricular segmentation in echocardiography.
• The proposed method offers a robust solution for enhancing cardiac diagnostic capabilities.
• Highlights the efficacy of diffusion models combined with attention mechanisms in medical image analysis.