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'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' 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.
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
Antonini A.Membro del Collaboration Group
;
2022
Abstract
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'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' 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.
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