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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Bibliographic Details
Published in:Arabian journal for science and engineering (2011) Vol. 44; no. 11; pp. 9249 - 9261
Main Authors: Sajid, Sidra, Hussain, Saddam, Sarwar, Amna
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2019
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-019-03967-8