Deep Learning and Multi-Sensor Fusion for Glioma Classification Using Multistream 2D Convolutional Networks
Paper in proceeding, 2018

This paper addresses issues of brain tumor, glioma, grading from multi-sensor images. Different types of scanners (or sensors) like enhanced T1-MRI, T2-MRI and FLAIR, show different contrast and are sensitive to different brain tissues and fluid regions. Most existing works use 3D brain images from single sensor. In this paper, we propose a novel multistream deep Convolutional Neural Network (CNN) architecture that extracts and fuses the features from multiple sensors for glioma tumor grading/subcategory grading. The main contributions of the paper are: (a) propose a novel multistream deep CNN architecture for glioma grading; (b) apply sensor fusion from T1-MRI, T2-MRI and/or FLAIR for enhancing performance through feature aggregation; (c) mitigate overfitting by using 2D brain image slices in combination with 2D image augmentation. Two datasets were used for our experiments, one for classifying low/high grade gliomas, another for classifying glioma with/without 1p19q codeletion. Experiments using the proposed scheme have shown good results (with test accuracy of 90.87% for former case, and 89.39 % for the latter case). Comparisons with several existing methods have provided further support to the proposed scheme.

FLAIR.

deep learning

glioma grading

T2-MR image

1p19q codeletion

sensor fusion

multi-stream convolutional neural networks

brain tumor classification

glioma

T1-MR image

Author

Chenjie Ge

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Irene Yu-Hua Gu

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Asgeir Jakola

Sahlgrenska University Hospital

Jie Yang

Shanghai Jiao Tong University

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

1557-170x (ISSN)

5894-5897
978-1-5386-3646-6 (ISBN)

40th Annual Int'l Conf of the IEEE Engineering in Medicine and Biology Society (EMBC18)
Honolulu, USA,

Areas of Advance

Life Science Engineering (2010-2018)

Subject Categories

Radiology, Nuclear Medicine and Medical Imaging

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing

DOI

10.1109/EMBC.2018.8513556

More information

Latest update

2/21/2022