Detection of lipid and fibrous plaques in optical coherence tomography images using deep learning

Irina A. Lakman; Irina A. Mustafina; Naufal Sh. Zagidullin; Roman I. Sladkov; Ruslan P. Ronzhin

doi:10.1117/12.2681409

4 October 2023 Detection of lipid and fibrous plaques in optical coherence tomography images using deep learning

Irina A. Lakman, Irina A. Mustafina, Naufal Sh. Zagidullin, Roman I. Sladkov, Ruslan P. Ronzhin

Proceedings Volume 12743, Optical Technologies for Telecommunications 2022; 1274311 (2023) https://doi.org/10.1117/12.2681409
Event: Optical Technologies for Telecommunications 2022, 2022, Ufa, Russian Federation

Abstract

The study goal was to evaluate the possibility of automating of optic coherent (OCT) images analysis and to develop a model for precise differentiation of nature of intracoronary atherosclerotic plaques using deep neural networks. At the first stage of the study, special software was developed that allows the experts to mark 16 different types of pathologies in the intracoronary images obtained from OCT. The following pipeline was used for image preprocessing: 1) image smoothing using a Gaussian filter (noise reduction), 2) image binarization using a threshold of values determined according to the Bradley-Roth adaptive (local) binarization method; 3) interpolation of missing values; 4) vascular wall determining; 5) transformation of polar coordinates into Cartesian ones. 534 images were collected for analysis, obtained as a result of OCT of the main 3 coronary vessels of the heart. Two models (convolutional neural networks with the AlexNet and ResNet-18 architecture) were trained to detect the pathologies. The results demonstrate high accuracy of the both models: plaques detection 0.72-1.00; lipid plaque - 0.875-0.929 and fibrous differentiation - 0.893-0.929.

Citation Download Citation

Irina A. Lakman, Irina A. Mustafina, Naufal Sh. Zagidullin, Roman I. Sladkov, and Ruslan P. Ronzhin "Detection of lipid and fibrous plaques in optical coherence tomography images using deep learning", Proc. SPIE 12743, Optical Technologies for Telecommunications 2022, 1274311 (4 October 2023); https://doi.org/10.1117/12.2681409

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Optical coherence tomography

Pathology

Education and training

Image processing

Arteries

Atherosclerosis

Image classification

Show All Keywords

Keywords/Phrases

Search In:

Publication Years