MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer’s Disease: A Survey

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.

[Retracted] A Prediction Model for Cognitive Impairment Risk in Colorectal Cancer after Chemotherapy Treatment

“…However, there are no systematic assessment tools to predict the risk for CRCI complications in colon cancer patients after chemotherapy [ 11 ]. CRCI risk prediction is now possible because of the advancements in computer science and technology [ 12 , 13 ]. A validated model can be developed to predict the CRCI risk in colorectal patients after chemotherapy.…”

Radiomics Analysis of Magnetic Resonance Imaging Facilitates the Identification of Preclinical Alzheimer’s Disease: An Exploratory Study

“…Identical conclusions were obtained in a recent large-scale multicenter study where the hippocampal features served as robust biomarkers for clinical identification of AD–dementia/MCI and further predicted whether MCI patients would convert to dementia ( Kun et al, 2020 ). In contrast, the deep learning method can indeed acquire slightly better diagnostic capabilities in the Alzheimer’s continuum ( Jo et al, 2019 ; Yamanakkanavar et al, 2020 ); however, it is difficult to explain the clinical correlations between these deep features and AD itself, and notably, Li et al (2017) have proved that the performance in identifying dementia from controls using radiomics is comparable to deep learning (91.4 and 93.9%, respectively). Here, in distinguishing preclinical AD patients or clinical converters, the accuracy of our models reached 81.9–95.9%, even higher than when distinguishing symptomatic patients from controls.…”

“…However, to the best of our knowledge, no such studies focusing on preclinical AD have been previously reported. Deep learning is another effective classification method, but it requires a large number of image datasets, and clinicians cannot obtain interpretable features as imaging biomarkers ( Yamanakkanavar et al, 2020 ); thus, we did not utilize this methodology.…”

MR Images, Brain Lesions, and Deep Learning

“…Hence, neuroimaging plays a fundamental role in understanding how the brain and the nervous system function [3] and discover how structural or functional anatomical alteration is correlated with different neurological disorders [4] and brain lesions. Currently, research on artificial intelligence (AI) and diverse techniques of imaging constitutes a crucial tool for studying the brain [5][6][7][8][9][10][11] and aids the physician to optimize the time-consuming tasks of detection and segmentation of brain anomalies [12] and also to better interpret brain images [13] and analyze complex brain imaging data [14].…”

“…In the past few years, there has been considerable research in the field of machine learning (ML) and deep learning (DL) to create automatic or semiautomatic systems, algorithms, and methods that allow detection of lesions in the brain, such as tumors, MS, stroke, glioma, AD, etc. [4,6,[8][9][10]26,28,30,36,[39][40][41][42][43][44][45][46][47][48]. Different studies demonstrate that deep learning algorithms can be successfully used for medical image retrieval, segmentation, computer-aided diagnosis, disease detection, and classification [49][50][51].…”