Differentiating between stable and progressive carotid atherosclerotic plaques from in-vivo ultrasound images using texture descriptors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00354530" target="_blank" >RIV/68407700:21230/21:00354530 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21460/21:00354530

Výsledek na webu

<a href="https://doi.org/10.1117/12.2605795" target="_blank" >https://doi.org/10.1117/12.2605795</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.2605795" target="_blank" >10.1117/12.2605795</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Differentiating between stable and progressive carotid atherosclerotic plaques from in-vivo ultrasound images using texture descriptors

Popis výsledku v původním jazyce

We describe an automatic pipeline for processing ultrasound images of the carotid artery, consisting of image type classification, carotid artery localization, segmentation, feature descriptor extraction, and plaque stability classification. The aim is to distinguish between stable (safe) and progressive (dangerous) atherosclerotic plaques from a single standard ultrasound transversal or longitudinal B-mode examination. The processing pipeline uses modern deep CNN techniques, while the descriptors are based on geometry and wavelets to characterize texture. When testing on a large dataset of 28718 images from 413 patients, we found that our automatically calculated descriptors are statistically significantly different between the two classes with a very high significance level, p<0.001. We have also created a random forest-based classifier to distinguish between progressive and stable plaques, although its accuracy remains low (61~62%).

Název v anglickém jazyce

Differentiating between stable and progressive carotid atherosclerotic plaques from in-vivo ultrasound images using texture descriptors

Popis výsledku anglicky

We describe an automatic pipeline for processing ultrasound images of the carotid artery, consisting of image type classification, carotid artery localization, segmentation, feature descriptor extraction, and plaque stability classification. The aim is to distinguish between stable (safe) and progressive (dangerous) atherosclerotic plaques from a single standard ultrasound transversal or longitudinal B-mode examination. The processing pipeline uses modern deep CNN techniques, while the descriptors are based on geometry and wavelets to characterize texture. When testing on a large dataset of 28718 images from 413 patients, we found that our automatically calculated descriptors are statistically significantly different between the two classes with a very high significance level, p<0.001. We have also created a random forest-based classifier to distinguish between progressive and stable plaques, although its accuracy remains low (61~62%).

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

17th International Symposium on Medical Information Processing and Analysis

ISBN

978-1-5106-5052-7

ISSN

0277-786X

e-ISSN

1996-756X

Počet stran výsledku

10

Strana od-do

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Název nakladatele

SPIE

Místo vydání

Bellingham (stát Washington)

Místo konání akce

Campinas

Datum konání akce

17. 11. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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