Brain Tumor Detection and Segmentation in MR Images Using Deep Learning

Gliomas are the most infiltrative and life-threatening brain tumors with exceptionally quick development. Gliomas segmentation using computer-aided diagnosis is a challenging task, due to irregular shape and diffused boundaries of tumor with the surrounding area. Magnetic resonance imaging (MRI) is...

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Vydáno v:	Arabian journal for science and engineering (2011) Ročník 44; číslo 11; s. 9249 - 9261
Hlavní autoři:	Sajid, Sidra, Hussain, Saddam, Sarwar, Amna
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2019 Springer Nature B.V
Témata:	Artificial neural networks Brain Brain cancer Deep learning Engineering Humanities and Social Sciences Image detection Image segmentation Machine learning Magnetic resonance imaging Medical imaging multidisciplinary NMR Nuclear magnetic resonance Post-production processing Research Article - Computer Engineering and Computer Science Science Tumors Deep learning Gliomas segmentation CNN Brain tumor segmentation MRI
ISSN:	2193-567X, 1319-8025, 2191-4281
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Abstract	Gliomas are the most infiltrative and life-threatening brain tumors with exceptionally quick development. Gliomas segmentation using computer-aided diagnosis is a challenging task, due to irregular shape and diffused boundaries of tumor with the surrounding area. Magnetic resonance imaging (MRI) is the most widely used method for imaging structures of interest in human brain. In this study, a deep learning-based method that uses different modalities of MRI is presented for the segmentation of brain tumor. The proposed hybrid convolutional neural network architecture uses patch-based approach and takes both local and contextual information into account, while predicting output label. The proposed network deals with over-fitting problem by utilizing dropout regularizer alongside batch normalization, whereas data imbalance problem is dealt with by using two-phase training procedure. The proposed method contains a preprocessing step, in which images are normalized and bias field corrected, a feed-forward pass through a CNN and a post-processing step, which is used to remove small false positives around the skull portion. The proposed method is validated on BRATS 2013 dataset, where it achieves scores of 0.86, 0.86 and 0.91 in terms of dice score, sensitivity and specificity for whole tumor region, improving results compared to the state-of-the-art techniques.
AbstractList	Gliomas are the most infiltrative and life-threatening brain tumors with exceptionally quick development. Gliomas segmentation using computer-aided diagnosis is a challenging task, due to irregular shape and diffused boundaries of tumor with the surrounding area. Magnetic resonance imaging (MRI) is the most widely used method for imaging structures of interest in human brain. In this study, a deep learning-based method that uses different modalities of MRI is presented for the segmentation of brain tumor. The proposed hybrid convolutional neural network architecture uses patch-based approach and takes both local and contextual information into account, while predicting output label. The proposed network deals with over-fitting problem by utilizing dropout regularizer alongside batch normalization, whereas data imbalance problem is dealt with by using two-phase training procedure. The proposed method contains a preprocessing step, in which images are normalized and bias field corrected, a feed-forward pass through a CNN and a post-processing step, which is used to remove small false positives around the skull portion. The proposed method is validated on BRATS 2013 dataset, where it achieves scores of 0.86, 0.86 and 0.91 in terms of dice score, sensitivity and specificity for whole tumor region, improving results compared to the state-of-the-art techniques.
Author	Hussain, Saddam Sajid, Sidra Sarwar, Amna
Author_xml	– sequence: 1 givenname: Sidra surname: Sajid fullname: Sajid, Sidra organization: Department of Software Engineering, University of Engineering and Technology Taxila – sequence: 2 givenname: Saddam surname: Hussain fullname: Hussain, Saddam email: saddam.hussain@cs.uol.edu.pk organization: Department of Computer Science and IT, University of Lahore – sequence: 3 givenname: Amna surname: Sarwar fullname: Sarwar, Amna organization: Department of Software Engineering, University of Engineering and Technology Taxila
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Copyright	King Fahd University of Petroleum & Minerals 2019 Copyright Springer Nature B.V. 2019
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Keywords	Deep learning Gliomas segmentation CNN Brain tumor segmentation MRI
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Snippet	Gliomas are the most infiltrative and life-threatening brain tumors with exceptionally quick development. Gliomas segmentation using computer-aided diagnosis...
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SubjectTerms	Artificial neural networks Brain Brain cancer Deep learning Engineering Humanities and Social Sciences Image detection Image segmentation Machine learning Magnetic resonance imaging Medical imaging multidisciplinary NMR Nuclear magnetic resonance Post-production processing Research Article - Computer Engineering and Computer Science Science Tumors
Title	Brain Tumor Detection and Segmentation in MR Images Using Deep Learning
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