Automated segmentation of intracranial carotid atherosclerosis in histological images: assessing the effect of staining

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00383575" target="_blank" >RIV/68407700:21230/25:00383575 - isvavai.cz</a>

Alternative codes found

RIV/61988987:17110/25:A2603DN4 RIV/00216208:11120/25:43928712 RIV/68407700:21460/25:00383575

Result on the web

<a href="https://doi.org/10.1117/12.3047219" target="_blank" >https://doi.org/10.1117/12.3047219</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.3047219" target="_blank" >10.1117/12.3047219</a>

Result language

angličtina

Original language name

Automated segmentation of intracranial carotid atherosclerosis in histological images: assessing the effect of staining

Original language description

Atherosclerosis, a major cause of ischemic stroke worldwide, is characterized by plaque formation, particularly in the carotid bifurcation, leading to arterial stenosis. Traditional histology and light microscopy have been used to study atherosclerotic plaques, but the advent of digital pathology and artificial intelligence has provided new opportunities. In this work, we proposed an automatic segmentation method using convolutional neural networks (U-Net and DeepLabV3+) to delineate atherosclerotic carotid plaque tissue. The study included 835 images of histological slices stained with hematoxylin and eosin and Van Gieson's method from 114 patients. The results showed that DeepLabV3+ outperforms UNet, achieving high accuracy for tissue types such as lumen, fibrous tissue, atheroma, calcification, and hemorrhage. Staining influenced segmentation results, with Van Gieson's stain excelling in fibrous tissue segmentation, while hematoxylin and eosin showed better results for calcification and hemorrhage. Moreover, the segmentation models facilitated clinical plaque classification, demonstrating good discrimination performance. Our study highlights the potential of deep neural networks in segmenting atherosclerotic plaques while emphasizing the need for careful consideration of staining effects in computerized analysis.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20602 - Medical laboratory technology (including laboratory samples analysis; diagnostic technologies) (Biomaterials to be 2.9 [physical characteristics of living material as related to medical implants, devices, sensors])

Project

<a href="/en/project/NV19-08-00362" target="_blank" >NV19-08-00362: Evaluation of atherosclerotic plaque stability in carotids using digital image analysis of ultrasound images</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

SPIE Medical Imaging 2025: Digital and Computational Pathology

ISBN

9781510686045

ISSN

1605-7422

e-ISSN

—

Number of pages

9

Pages from-to

—

Publisher name

SPIE

Place of publication

Bellingham (stát Washington)

Event location

San Diego, California,

Event date

Feb 16, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001511213700003