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

Kód výsledku v 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>

Nalezeny alternativní kódy

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

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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])

Projekt

<a href="/cs/project/NV19-08-00362" target="_blank" >NV19-08-00362: Hodnocení stability aterosklerotického plátu v karotidě pomocí digitální analýzy ultrazvukového obrazu u pacientů se stenózou vnitřní karotidy</a><br>

Návaznosti

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

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

SPIE Medical Imaging 2025: Digital and Computational Pathology

ISBN

9781510686045

ISSN

1605-7422

e-ISSN

—

Počet stran výsledku

9

Strana od-do

—

Název nakladatele

SPIE

Místo vydání

Bellingham (stát Washington)

Místo konání akce

San Diego, California,

Datum konání akce

16. 2. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001511213700003