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Recent advancement in learning methodology for segmenting brain tumor from magnetic resonance imaging -a review

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Abstract

Glioblastomata are the most generally perceived fundamental brain malignant tumors known as Gliomas, with different shape, size & sub regions. It is hard to segment all three different sub regions perfectly. In any case, when the exact segmentation is perceived at the beginning stage, appropriate treatment should be possible. To overcome this issue, Robust learning strategy is needed for brain tumor segmentation to distinguish all three different regions including shape and size which help the specialist to plan for a surgery. Comparative studies for brain tumor segmentation & detection using machine learning & deep Learning methods are presented in this survey. Simulation results of some widely used deep learning methods are also covered & discussed. Our survey covers 107 studies using different learning methods along with Methodology, Dataset and Dice similarity coefficient Achieved. Out of 107 review studies, Robust unpaired generative adversarial network gives better performance for all three regions by securing more than 90% dice score than the other existing schemes but with large computation cost. Ensembled 3D variants, Hybrid convolutional neural network & U-Net variants gives better segmentation compared to existing studies with moderate computational cost. The existing methods was able to decide tumor yet doesn’t find volumetric measures of tumor in three orthogonal planes. In any case, regardless of segmentation accuracy, the current techniques don’t meet the robustness levels needed for patient-focused clinical use. Finally, the paper concludes with some research directions & research gaps that should be followed & solved in the future for further improvement in medical image segmentation & detection.

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Abbreviations

SVM:

Support Vector Machine

GNN:

Graphical Neural Network

CNN:

Convolution neural network

MRI:

Magnetic resonance imaging

CT:

Computerized tomography

ADC:

Apparent diffusion coefficient

HGG:

High-grade glioma

LGG:

Low-grade glioma

NET:

Nonenhanced tumor

ED:

Edema

NCR:

Necrotic core

RNN:

Recurrent neural network

GAN:

Generative adversarial network

MICCAI:

The Medical Image Computing and Computer Assisted Intervention Society

NIfTI:

Neuroimaging Informatics Technology Initiative

PubMed:

Public/Publisher MEDLINE

MEDLINE:

Medical Literature Analysis and Retrieval System Online

CSF:

Cerebrospinal Fluid

GM:

Gray Matter

WM:

White Matter

ReLU:

Rectified Linear Unit

ROI:

Region of interest

LSTM:

Long short-term memory

CRF:

Conditional Random Fields

HD:

Hausdorff Distance

CLSTM:

Contextual LSTM

GRU:

Gated recurrent unit

FCN:

Fully Convolutional Network

FCNN:

Fully convolutional neural network

WT:

Whole Tumor

ET:

Enhancing Tumor

TC:

Tumor Core

HNF:

Hybrid High-resolution and Non-local Feature Network

GPU:

Graphics processing unit

DRL:

Deep reinforcement learning

SLIC:

Simple Linear Iterative Clustering

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    Sunayana G. Domadia

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    Falgunkumar N. Thakkar & Mayank A. Ardeshana

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Domadia, S.G., Thakkar, F.N. & Ardeshana, M.A. Recent advancement in learning methodology for segmenting brain tumor from magnetic resonance imaging -a review. Multimed Tools Appl 82, 34809–34845 (2023). https://doi.org/10.1007/s11042-023-14857-5

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