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The potential of improving disease detection and treatment planning comes with accurate and fully automatic algorithms for brain tumor segmentation. Glioma, a type of brain tumor, can appear at different locations with different shapes and sizes. Manual segmentation of brain tumor regions is not only time-consuming but also prone to human error, and its performance depends on pathologists’ experience. In this paper, we tackle this problem by applying a fully convolutional neural network SegNet to 3D data sets for four MRI modalities (Flair, T1, T1ce, and T2) for automated segmentation of brain tumor and sub-tumor parts, including necrosis, edema, and enhancing tumor. To further improve tumor segmentation, the four separately trained SegNet models are integrated by post-processing to produce four maximum feature maps by fusing the machine-learned feature maps from the fully convolutional layers of each trained model. The maximum feature maps and the pixel intensity values of the original MRI modalities are combined to encode interesting information into a feature representation. Taking the combined feature as input, a decision tree (DT) is used to classify the MRI voxels into different tumor parts and healthy brain tissue. Evaluating the proposed algorithm on the dataset provided by the Brain Tumor Segmentation 2017 (BraTS 2017) challenge, we achieved F-measure scores of 0.85, 0.81, and 0.79 for whole tumor, tumor core, and enhancing tumor, respectively.

Experimental results demonstrate that using SegNet models with 3D MRI datasets and integrating the four maximum feature maps with pixel intensity values of the original MRI modalities has potential to perform well on brain tumor segmentation.


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Automated brain tumor segmentation on multi-modal MR image using SegNet

Show Author's information Salma Alqazzaz1,2( )Xianfang Sun3Xin Yang1Len Nokes1
School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK.
Department of Physics, College of Science for Women, Baghdad University, Baghdad, Iraq.
School of Computer Science and Informatics, Cardiff University, Cardiff, CF24 3AA, UK.

Abstract

The potential of improving disease detection and treatment planning comes with accurate and fully automatic algorithms for brain tumor segmentation. Glioma, a type of brain tumor, can appear at different locations with different shapes and sizes. Manual segmentation of brain tumor regions is not only time-consuming but also prone to human error, and its performance depends on pathologists’ experience. In this paper, we tackle this problem by applying a fully convolutional neural network SegNet to 3D data sets for four MRI modalities (Flair, T1, T1ce, and T2) for automated segmentation of brain tumor and sub-tumor parts, including necrosis, edema, and enhancing tumor. To further improve tumor segmentation, the four separately trained SegNet models are integrated by post-processing to produce four maximum feature maps by fusing the machine-learned feature maps from the fully convolutional layers of each trained model. The maximum feature maps and the pixel intensity values of the original MRI modalities are combined to encode interesting information into a feature representation. Taking the combined feature as input, a decision tree (DT) is used to classify the MRI voxels into different tumor parts and healthy brain tissue. Evaluating the proposed algorithm on the dataset provided by the Brain Tumor Segmentation 2017 (BraTS 2017) challenge, we achieved F-measure scores of 0.85, 0.81, and 0.79 for whole tumor, tumor core, and enhancing tumor, respectively.

Experimental results demonstrate that using SegNet models with 3D MRI datasets and integrating the four maximum feature maps with pixel intensity values of the original MRI modalities has potential to perform well on brain tumor segmentation.

Keywords:

brain tumor segmentation, multi-modal MRI, convolutional neural networks, fully convolutional networks, decision tree
Revised: 07 February 2019 Accepted: 24 March 2019 Published: 23 April 2019 Issue date: June 2019
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Publication history

Revised: 07 February 2019
Accepted: 24 March 2019
Published: 23 April 2019
Issue date: June 2019

Copyright

© The author(s) 2019

Acknowledgements

We would like to thank nVidia for their kind donation of a Titan XP GPU.

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