Abstract
Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer’s disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD.
Highlights
Magnetic resonance imaging (MRI) is used to analyze the anatomical structures of the brain due to its high spatial resolution and ability to contrast soft tissue
It is known that MRI is generally associated with fewer health risks compared to other modalities like computed tomography (CT) and positron emission tomography (PET) [1]
This paper aims to provide an outline of progressive deep learning methods in the area of MRI
Summary
Magnetic resonance imaging (MRI) is used to analyze the anatomical structures of the brain due to its high spatial resolution and ability to contrast soft tissue. Over the past few decades, tremendous progress has been made in assessing brain injuries and exploring brain anatomy with MRI [2]. Disorders such as Alzheimer’s disease (AD) and multiple sclerosis [3] associated with the brain can be identified using MRI. Tissue atrophy is a popular indicator that is used in diagnosing AD. Accurate detection and classification of unhealthy tissue and its surrounding healthy structures are important in the diagnosis of conditions such as AD. A large amount of data is required for more accurate diagnoses
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