Deep learning model for intravascular ultrasound image segmentation with temporal consistency

Area of Science:

• Cardiovascular Imaging
• Artificial Intelligence in Medicine
• Medical Image Analysis

Background:

• Intravascular ultrasound (IVUS) is crucial for assessing coronary artery disease.
• Accurate delineation of lumen and external elastic membrane (EEM) is essential for quantitative analysis.
• Current manual segmentation can be time-consuming and subject to variability.

Purpose of the Study:

• To develop and validate a deep learning model for automated segmentation of IVUS images.
• To evaluate the model's performance against expert analysis and its clinical impact on cardiovascular events.

Main Methods:

• Developed a deep learning model using 1240 IVUS pullbacks (191,407 frames) for lumen and EEM segmentation.
• Validated the model on independent datasets, assessing frame- and vessel-level performance.
• Evaluated clinical utility by correlating model-derived metrics with 3-year cardiovascular events.

Main Results:

• Achieved high Dice similarity coefficients (DSC) for lumen (0.966 ± 0.025) and EEM (0.982 ± 0.017) in the test set.
• Demonstrated excellent performance even with image attenuation and strong agreement with expert measurements (intra-class coefficients > 0.94).
• Model-derived metrics predicted 3-year cardiac events, including cardiac death and target vessel revascularization.

Conclusions:

• The deep learning model provides accurate and consistent delineation of coronary artery geometry from IVUS images.
• The model shows potential for cost savings and enhanced clinical decision-making in cardiovascular care.
• This AI tool may improve the prediction of adverse cardiovascular events.