Comprehensive full-vessel segmentation and volumetric plaque quantification for intracoronary optical coherence...

Area of Science:

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

Background:

• Intracoronary optical coherence tomography (OCT) offers detailed coronary lesion insights.
• Manual OCT image interpretation is labor-intensive and prone to interobserver variability.

Purpose of the Study:

• To develop and validate a deep learning-based algorithm (OCT-AID) for multiclass semantic segmentation of intracoronary OCT images.
• To enhance the efficiency and standardization of OCT image interpretation.

Main Methods:

• Manual annotation of OCT images for nine classes: guidewire artefact, lumen, side branch, intima, media, lipid plaque, calcified plaque, thrombus, plaque rupture, and background.
• Development and validation of a deep learning algorithm (OCT-AID) using training, internal test, and independent external test datasets.
• Performance evaluation using Dice scores for segmentation and Cohen's kappa (κ) for frame-wise identification of lipid and calcified plaques.

Main Results:

• The OCT-AID algorithm achieved a mean Dice score of 0.659 overall and 0.757 on true-positive frames in the internal test set.
• Substantial to almost perfect agreement was observed for lipid (κ=0.817) and calcified plaque (κ=0.795) identification.
• Strong performance was maintained in the external test set, with κ-values of 0.720 for lipid and 0.851 for calcified plaques.

Conclusions:

• The developed multiclass semantic segmentation algorithm shows significant promise for interpreting intracoronary OCT images, including challenging cases with artefacts or destabilized plaques.
• OCT-AID represents a crucial advancement toward standardized and comprehensive analysis of OCT imaging data.