Deep Learning and Multi-Sensor Fusion for Glioma Classification Using Multistream 2D Convolutional Networks
Paper i 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.

multi-stream convolutional neural networks

T1-MR image

glioma

T2-MR image

deep learning

glioma grading

sensor fusion

1p19q codeletion

FLAIR.

brain tumor classification

Författare

Chenjie Ge

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Irene Yu-Hua Gu

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Asgeir Jakola

Sahlgrenska universitetssjukhuset

Jie Yang

Shanghai Jiao Tong University

4 pages-

40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18)
,USA, ,

Styrkeområden

Livsvetenskaper och teknik

Ämneskategorier

Radiologi och bildbehandling

Datorseende och robotik (autonoma system)

Medicinsk bildbehandling