Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiovascular Diagnostics

Background:

• Accurate coronary vessel segmentation is crucial for automated analysis of coronary angiographies.
• Challenges include uneven contrast filling and background noise, hindering precise segmentation.
• Existing methods often struggle with the complexity and variability of angiographic images.

Purpose of the Study:

• To develop and evaluate an advanced deep learning model for accurate coronary vessel segmentation.
• To enhance feature extraction and image quality for improved segmentation performance.
• To assess the model's efficiency and robustness for potential clinical application.

Main Methods:

• An ensemble U-Net model (SE-RegUNet) was developed, incorporating RegNet encoders and squeeze-and-excitation blocks.
• A dual-phase image preprocessing strategy (unsharp masking, adaptive histogram equalization) was employed.
• The model underwent fivefold cross-validation, Ranger21 optimization, and external validation on the DCA1 dataset.

Main Results:

• The SE-RegUNet 4GF model achieved a Dice score of 0.72 and accuracy of 0.97 on internal validation.
• External validation on the DCA1 dataset yielded a Dice score of 0.76 and accuracy of 0.97.
• The model demonstrated efficient image processing at 41.6 frames per second.

Conclusions:

• SE-RegUNet provides a robust and accurate method for coronary vessel segmentation in angiographic images.
• The model's performance indicates significant potential for improving automated assessment in cardiology.
• Further clinical validation is required for routine medical practice implementation.