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Comprehensive Review on MRI-Based Brain Tumor Segmentation: A Comparative Study from 2017 Onwards

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Abstract

Brain tumor segmentation has been a challenging and popular research problem in the area of medical imaging and computer-aided diagnosis. In the last few years, especially since 2017, researchers have significantly contributed for solving and enhancing the performance of brain tumor abnormality detection and tumor segmentation from magnetic resonance (MR) images. This paper presents a detailed and intensive review of automated brain disease diagnosis and tumor segmentation methods obtained by investigating numerous recent articles. In the first phase, an extensive literature search is conducted with more than 600 articles from medical image analysis, brain disease diagnosis, and tumor segmentation. Around 50% of articles are removed after initial scanning based on certain criteria, i.e., publication year, number of citations, and bibliographic indexing. A total of 161 relevant articles are finally selected in the second phase based on their performance and novelty of the proposed methods. Furthermore, the selected articles are investigated from the perspectives of methodology and performance. Overall methods exploited for brain disease detection and tumor segmentation are categorised into three broad classes, i.e., conventional methods, machine learning-based methods, and deep learning-based methods. As deep learning-based methods are state-of-the-art for computer-aided diagnosis (CAD) nowadays, we investigated several deep learning models, such as the convolutional neural network (CNN), the generative adversarial network (GAN), the U-Net, etc., along with residual block and attention gate, with respect to their learning mechanisms and hyper-parameter tuning. Methods from each class are rigorously reviewed and summarised by identifying their advantages, disadvantages, dataset, MR modality used, and type of images (2D/3D) processed. The methods are also analysed and compared based on their performance in various measures such as dice similarity coefficient (DSC), sensitivity, positive predictive value (PPV), Specificity, Jaccard Index (JI), Accuracy, Hausdorff distance, and computation time. In this review, the high heterogeneity of articles based on different methodologies is considered in light of the recent progress and development of brain tumor detection and segmentation. During analysis, it has been observed that deep learning-based methods, especially various variants of the U-Net model, outperform other approaches for brain tumor segmentation.

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Data availability

The dataset used in this article is provided by Brain Tumor Segmentation Challenge 2020. The dataset is publicly available at https://www.med.upenn.edu/cbica/brats2020/data.html. There is no additional data to be shared about the publication of this article. Everything is given within the article.

Notes

  1. http://www.jmir.org/2013/11/e245/ (Accessed on October 17, 2023.).

  2. https://www.smir.ch/BRATS/Start2015 (Accessed on October 17, 2023.).

Abbreviations

AC:

Accuracy

ACM:

Active contour model

ACRC:

Adaptive convex region contour

AGC:

Adaptive gamma correction

ANN:

Artificial neural network

ASAR:

Adaptive search and rescue algorithm

AWGN:

Additive white Gaussian noise

BN:

Batch normalization

BWT:

Berkeley wavelet transformation

CSF:

Cerebrospinal fluid

CRF:

Conditional random field

CAD:

Computer-aided diagnosis

CLAHE:

Contrast-limited adaptive histogram equalization

CNN:

Convolutional neural network

CNS:

Central nervous system

CT:

Computed tomography

CWT:

Complex wavelet transform

DCT:

Discrete cosine transformation

DICOM:

Digital imaging and communications in medicine

DNN:

Deep neural network

DSC:

Dice similarity coefficient

DWI:

Diffusion-weighted imaging

DWT:

Discrete wavelet transform

ELM:

Extreme learning machine

EM:

Expectation maximization

ERT:

Extremely randomized tree

FCM:

Fuzzy C-means

FN:

False negative

FP:

False positive

FFNN:

Feed forward neural network

FLAIR:

Fluid-attenuated inversion recovery

FPN:

Feature Pyramid Network

FRN:

Filter response normalization

GA:

Genetic algorithm

GAN:

Generative adversarial network

GBM:

Glioblastoma multiforme

GM:

Gray matter

GLCM:

Gray level co-occurrence matrix

GLRM:

Gray level run-length matrix

GOA:

Grasshopper optimization algorithm

GSP:

Ghost spatial pyramid

GWO:

Gray wolf optimization

HGG:

High-grade glioma

TP:

True positive

HOG:

Histogram of oriented gradients

JI:

Jaccard Index

J-TSA:

Jaya-tunicate swarm algorithm

kNN:

k-nearest neighbor

LBM:

Lattice–Boltzmann method

LBP:

Local binary patterns

LDA:

Linear discriminant analysis

LGG:

Low-grade glioma

mBm:

Multifractal Brownian motion

MICCAI:

Medical Image Computing and Computer Assisted Intervention Society

MIP:

Minimum intensity projection

MLP:

Multi-layer perceptron

MR:

Magnetic resonance

MRA:

Magnetic resonance angiography

MRI:

Magnetic resonance imaging

MRWA:

Modified regularized winnow algorithm

MS:

Multiple sclerosis

MSE:

Mean square error

PMRSi:

Proton magnetic resonance spectroscopic image

PSO:

Particle swarm optimization

PET:

Positron emission tomography

RELM:

Regularized extreme learning machine

ROI:

Region of interest

RWA:

Regularized winnow algorithm

PCA:

Principal component analysis

PD:

Proton density-weighted MRI

PPV:

Positive predictive value

PWI:

Perfusion-weighted imaging

RBF:

Radial basis function

RF:

Random forest

ROI:

Region of interest

SGSA:

Skippy greedy snake algorithm

SOM:

Self organizing map

SP:

Specificity

SVD:

Singular value decomposition

SVM:

Support vector machine

SW:

Susceptibility-weighted

SWT:

Stationary wavelet transform

T1C:

Post contrast T1-weighted image

TCIA:

The Cancer Imaging Archive

TN:

True negative

VAE:

Variational auto-encoders

WHO:

World Health Organization

WM:

White matter

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  1. School of Computer Science, University of Petroleum and Energy Studies, Bidholi via Premnagar, Dehradun, Uttarakhand, 248006, India

    Amit Verma

  2. School of Computer Science Engineering and Technology, Bennett University, Plot Nos 8-11, TechZone II, Greater Noida, Uttar Pradesh, 201310, India

    Shiv Naresh Shivhare, Shailendra P. Singh & Naween Kumar

  3. Faculty of Information Technology, Graduate School, Duy Tan University, Da Nang, Da Nang, 550000, Vietnam

    Anand Nayyar

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Verma, A., Shivhare, S.N., Singh, S.P. et al. Comprehensive Review on MRI-Based Brain Tumor Segmentation: A Comparative Study from 2017 Onwards. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10128-0

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