A study on 3D classical versus GAN-based augmentation for MRI brain image to predict the diagnosis of dementia with Lewy bodies and Alzheimer's disease in a European multi-center study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064203%3A_____%2F22%3A10445370" target="_blank" >RIV/00064203:_____/22:10445370 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11130/22:10445370

Výsledek na webu

<a href="https://doi.org/10.1117/12.2611339" target="_blank" >https://doi.org/10.1117/12.2611339</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.2611339" target="_blank" >10.1117/12.2611339</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A study on 3D classical versus GAN-based augmentation for MRI brain image to predict the diagnosis of dementia with Lewy bodies and Alzheimer's disease in a European multi-center study

Popis výsledku v původním jazyce

Every year around 10 million people are diagnosed with dementia worldwide. Higher life expectancy and population growth could inflate this number even further in the near future. Alzheimer&apos;s disease (AD) is one of the primary and most frequently diagnosed dementia disease in elderly subjects. On the other hand, dementia with Lewy Bodies (DLB) is the third most common cause of dementia. A timely and accurate diagnosis of dementia is critical for patients&apos; management and treatment. However, its diagnostic is often challenging due to overlapping symptoms between the different forms of thee disease. Deep learning (DL) combined with magnetic resonance imaging (MRI) has shown potential improving the diagnostic accuracy of several neurodegenerative diseases. In spite of it, DL methods heavily rely on the availability of annotated data. Classic augmentation techniques such as translation are commonly used to increase data availability. In addition, synthetic samples obtained through generative adversarial networks (GAN) are becoming an alternative to classic augmentation. Such techniques are well-known and explored for 2D images, but little is known about their effects in a 3D setting. In this work, we explore the effects of 3D classic augmentation and 3D GAN-based augmentation to classify between AD, DLB and control subjects.

Název v anglickém jazyce

A study on 3D classical versus GAN-based augmentation for MRI brain image to predict the diagnosis of dementia with Lewy bodies and Alzheimer's disease in a European multi-center study

Popis výsledku anglicky

Every year around 10 million people are diagnosed with dementia worldwide. Higher life expectancy and population growth could inflate this number even further in the near future. Alzheimer&apos;s disease (AD) is one of the primary and most frequently diagnosed dementia disease in elderly subjects. On the other hand, dementia with Lewy Bodies (DLB) is the third most common cause of dementia. A timely and accurate diagnosis of dementia is critical for patients&apos; management and treatment. However, its diagnostic is often challenging due to overlapping symptoms between the different forms of thee disease. Deep learning (DL) combined with magnetic resonance imaging (MRI) has shown potential improving the diagnostic accuracy of several neurodegenerative diseases. In spite of it, DL methods heavily rely on the availability of annotated data. Classic augmentation techniques such as translation are commonly used to increase data availability. In addition, synthetic samples obtained through generative adversarial networks (GAN) are becoming an alternative to classic augmentation. Such techniques are well-known and explored for 2D images, but little is known about their effects in a 3D setting. In this work, we explore the effects of 3D classic augmentation and 3D GAN-based augmentation to classify between AD, DLB and control subjects.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

ISBN

978-1-5106-4941-5

ISSN

1605-7422

e-ISSN

2410-9045

Počet stran výsledku

10

Strana od-do

—

Název nakladatele

SPIE

Místo vydání

Bellingham

Místo konání akce

San Diego

Datum konání akce

21. 3. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000838048600084