Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

Kong, Zhenglun; Luo, Jie; Xu, Shengpu; Li, Ting

doi:10.1117/12.2293050

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…There are four types of predicted classes: GM, WM, CSF, and background. For a given input image, the proposed model creates a respective learned representation (6). Further, the input image is categorized into one of four output classes using this feature representation.…”

Section: Classification Layermentioning

confidence: 99%

“…Some of the advantages of CNN are explained as (i) application-specific activation functions can overcome the training problems, (ii) various dropout techniques can aid in neural network regularization, and (iii) many optimization methods can be used for efficient training of the CNN models [4][5]. Kong et al [6] proposed the use of deep learning for automatic tissue segmentation, where MRI images were pre-processed with the wavelet multi-scale transformation, and then segmentation was performed using CNN. Shakeri et al [7] proposed the method of segmenting objects present from natural images using a fully CNN (FCNN).…”

Section: Introductionmentioning

confidence: 99%

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A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation

2022

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Section: Classification Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation

2022

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“…Semantic-wise Dong [38] Brosch [39] Shakeri [40] Zhenglun [41] Milletari [42] Raghav [43] The main objective of the semantic-wise segmentation is to link each pixel of an image with its class label. It is called dense prediction because every pixel is predicted from the whole input image.…”

Section: Strategies Authors Descriptionmentioning

confidence: 99%

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

Yamanakkanavar

Choi

Lee

2020

Sensors

145

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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.

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“…Due to advancements in GPU processing power, large quantities of imaging data may now be used for training, resulting in improved accuracy despite cosmetic alterations when employing deep learning methods. Deep learning is essential to numerous fields and technologies, such as image segmentation, genotype/phenotype detection, disease categorization, object detection, and speech recognition [31], [32], [33], [34]. The terms ''deep Boltzmann machines,'' ''convolutional neural networks,'' ''stacked auto-encoders,'' and ''deep neural networks'' all refer to popular deep learning approaches.…”

Section: A Introduction To the Structure Of Cnnsmentioning

confidence: 99%

A Review on Alzheimer’s Disease Through Analysis of MRI Images Using Deep Learning Techniques

Aparna¹

2023

IEEE Access

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The anatomical structure of the brain has been studied with the help of magnetic resonance imaging (MRI), which has been used to analyze numerous neurological diseases and define pathological areas. Early detection of Alzheimer's Disease (AD) patients is critical in order to implement preventative measures. Alzheimer's disease (AD) is the most common chronic disease in the elderly, with a high incidence rate. In recent years, deep learning has seen a lot of success in the medical image analysis. Brain diseases can be more accurately categorized using segmented MRI scans due to in-depth analyses of tissue architecture. Many, complex segmentation approaches have been presented for AD diagnosis. Since deep learning algorithms can yield effective results over a large data collection, they have received interest for use in segmenting the brain's structure and classifying AD. Consequently, the deep learning techniques are currently favored over machine learning techniques. We discuss how convolutional neural network concepts can be used to study brain anatomy in order to detect AD. New techniques, their results on open datasets, and the benefits of brain MRI segmentation for Alzheimer's disease categorization are discussed. In this article, the literature on Alzheimer's disease is briefly reviewed, and the possibility of Deep Learning to improve early diagnosis is discussed.INDEX TERMS Alzheimer's disease, brain analysis, classification techniques, deep learning techniques, image processing techniques.

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Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

Cited by 5 publications

References 10 publications

A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation

A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation

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

A Review on Alzheimer’s Disease Through Analysis of MRI Images Using Deep Learning Techniques

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