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Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 8356294, 7 pages
Research Article

Multiscale CNNs for Brain Tumor Segmentation and Diagnosis

Multimedia Information Processing Group, College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing, China

Received 23 October 2015; Revised 28 January 2016; Accepted 2 February 2016

Academic Editor: Syoji Kobashi

Copyright © 2016 Liya Zhao and Kebin Jia. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs). Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness.