Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture

Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, making it an im...

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Autores principales:	Anuja Arora, Ambikesh Jayal, Mayank Gupta, Prakhar Mittal, Suresh Chandra Satapathy
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	brain tumor segmentation deep learning U-Net BraTs 2018 MRI Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/0ced71a1adad4606a45463ea5d442eda
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spelling	oai:doaj.org-article:0ced71a1adad4606a45463ea5d442eda2021-11-25T17:17:22ZBrain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture10.3390/computers101101392073-431Xhttps://doaj.org/article/0ced71a1adad4606a45463ea5d442eda2021-10-01T00:00:00Zhttps://www.mdpi.com/2073-431X/10/11/139https://doaj.org/toc/2073-431XBrain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, making it an important tool for effective diagnosis which is requisite to replace the existing manual detection system where patients rely on the skills and expertise of a human. In order to solve this problem, a brain tumor segmentation & detection system is proposed where experiments are tested on the collected BraTS 2018 dataset. This dataset contains four different MRI modalities for each patient as T1, T2, T1Gd, and FLAIR, and as an outcome, a segmented image and ground truth of tumor segmentation, i.e., class label, is provided. A fully automatic methodology to handle the task of segmentation of gliomas in pre-operative MRI scans is developed using a U-Net-based deep learning model. The first step is to transform input image data, which is further processed through various techniques—subset division, narrow object region, category brain slicing, watershed algorithm, and feature scaling was done. All these steps are implied before entering data into the U-Net Deep learning model. The U-Net Deep learning model is used to perform pixel label segmentation on the segment tumor region. The algorithm reached high-performance accuracy on the BraTS 2018 training, validation, as well as testing dataset. The proposed model achieved a dice coefficient of 0.9815, 0.9844, 0.9804, and 0.9954 on the testing dataset for sets HGG-1, HGG-2, HGG-3, and LGG-1, respectively.Anuja AroraAmbikesh JayalMayank GuptaPrakhar MittalSuresh Chandra SatapathyMDPI AGarticlebrain tumor segmentationdeep learningU-NetBraTs 2018MRIElectronic computers. Computer scienceQA75.5-76.95ENComputers, Vol 10, Iss 139, p 139 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	brain tumor segmentation deep learning U-Net BraTs 2018 MRI Electronic computers. Computer science QA75.5-76.95
spellingShingle	brain tumor segmentation deep learning U-Net BraTs 2018 MRI Electronic computers. Computer science QA75.5-76.95 Anuja Arora Ambikesh Jayal Mayank Gupta Prakhar Mittal Suresh Chandra Satapathy Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture
description	Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, making it an important tool for effective diagnosis which is requisite to replace the existing manual detection system where patients rely on the skills and expertise of a human. In order to solve this problem, a brain tumor segmentation & detection system is proposed where experiments are tested on the collected BraTS 2018 dataset. This dataset contains four different MRI modalities for each patient as T1, T2, T1Gd, and FLAIR, and as an outcome, a segmented image and ground truth of tumor segmentation, i.e., class label, is provided. A fully automatic methodology to handle the task of segmentation of gliomas in pre-operative MRI scans is developed using a U-Net-based deep learning model. The first step is to transform input image data, which is further processed through various techniques—subset division, narrow object region, category brain slicing, watershed algorithm, and feature scaling was done. All these steps are implied before entering data into the U-Net Deep learning model. The U-Net Deep learning model is used to perform pixel label segmentation on the segment tumor region. The algorithm reached high-performance accuracy on the BraTS 2018 training, validation, as well as testing dataset. The proposed model achieved a dice coefficient of 0.9815, 0.9844, 0.9804, and 0.9954 on the testing dataset for sets HGG-1, HGG-2, HGG-3, and LGG-1, respectively.
format	article
author	Anuja Arora Ambikesh Jayal Mayank Gupta Prakhar Mittal Suresh Chandra Satapathy
author_facet	Anuja Arora Ambikesh Jayal Mayank Gupta Prakhar Mittal Suresh Chandra Satapathy
author_sort	Anuja Arora
title	Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture
title_short	Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture
title_full	Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture
title_fullStr	Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture
title_full_unstemmed	Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture
title_sort	brain tumor segmentation of mri images using processed image driven u-net architecture
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0ced71a1adad4606a45463ea5d442eda
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Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture

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