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Multiscale CNNs for Brain Tumor Segmentation and Diagnosis
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-03-16
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhao, Liya& Jia, Kebin. 2016. Multiscale CNNs for Brain Tumor Segmentation and Diagnosis. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1100209
Modern Language Association (MLA)
Zhao, Liya& Jia, Kebin. Multiscale CNNs for Brain Tumor Segmentation and Diagnosis. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1100209
American Medical Association (AMA)
Zhao, Liya& Jia, Kebin. Multiscale CNNs for Brain Tumor Segmentation and Diagnosis. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1100209
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100209