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. 2025 Mar 18;4(3Part B):102524.
doi: 10.1016/j.jscai.2024.102524. eCollection 2025 Mar.

Histology-Grounded Automated Plaque Subtype Segmentation in Intravascular Optical Coherence Tomography

Paul Young  1 , Drew Nolen  2 , Thomas E Milner  3 , Alexandra Gruslova  2 , Deborah Vela  4 , Louis Maximilian Buja  4 , Luis A Diaz Sanmartin  2 , Paul Rad  1 , Marc D Feldman  2
Affiliations

Histology-Grounded Automated Plaque Subtype Segmentation in Intravascular Optical Coherence Tomography

Paul Young et al. J Soc Cardiovasc Angiogr Interv. .

Abstract

Background: Intravascular optical coherence tomography (IVOCT) adoption has been limited by the complexity of image interpretation. The interpretation of histologic subtypes beyond lipid, calcium, and fibrous is challenging to human readers. To assist and standardize IVOCT image analysis, we demonstrate an artificial intelligence algorithm based on a histology data set that identifies lipid pools, fibrofatty, calcified lipid, and calcified fibrous in human coronary arteries for the first time.

Methods: Sixty-seven human coronary arteries were imaged with IVOCT within 24 hours after death and then underwent histologic examination. IVOCT images were coregistered and segmented into histologic subtypes: lipid pools, fibrofatty tissue, calcified lipid, and calcified fibrous tissue. Experiments regarding lipidic plaque included fibrofatty tissue, lipid pools, and calcified lipids. Experiments regarding calcium plaque included calcified fibrous and calcified lipid plaques. Optical coherence tomography images were lumen justified and cropped to a depth of 200 pixels (1 mm) to account for limited optical coherence tomography penetration depth. IVOCT segmentations from expert readers guided by histology were used to train segmentation neural networks.

Results: For each data set, in addition to testing each of these subtypes individually, we trained and tested the model on the combined grouping of subtypes. Combined lipid subtypes achieved validation and test Dice (Sørensen-Dice coefficient) of 0.63 and 0.40, respectively, whereas combined calcium subtypes achieved validation and test Dice of 0.66 and 0.62, respectively.

Conclusions: This histology-validated artificial intelligence algorithm driven by histologic subtypes can identify plaque subtypes not evident to a human reader. The reported algorithm can provide a fast solution to IVOCT image interpretation.

Keywords: artificial intelligence; histology; intravascular optical coherence tomography; machine learning; neural networks; plaque classification.

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Figures

Central Illustration
Central Illustration
Summaryworkflow. Arteries used to develop this approach were imaged with Abbott ILUMIEN and analyzed with histology. Polar optical coherence tomography (OCT) images are flattened at the lumen and cropped to 200 pixels (∼ 1 mm) to focus on relevant regions. Images are then processed by the segmentation models for each plaque type to create the predictions for each plaque type. Predictions are then reshaped into their original form and converted to cartesian for viewing. OCT images are log-transformed for better visualization.
Figure 1
Figure 1
Histological annotation of 4 plaque components. To enable the detection of various plaque types and their subtypes, 820 frames from 67 arteries from 42 hearts were segmented into 5 plaque subtypes with the assistance of histology. Optical coherence tomography (OCT) images are log-transformed for better visualization.
See this image and copyright information in PMC

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