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Multiscale CNNs for Brain Tumor Segmentation and Diagnosis
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4812495/ https://www.ncbi.nlm.nih.gov/pubmed/27069501 http://dx.doi.org/10.1155/2016/8356294 |
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author | Zhao, Liya Jia, Kebin |
author_facet | Zhao, Liya Jia, Kebin |
author_sort | Zhao, Liya |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4812495 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-48124952016-04-11 Multiscale CNNs for Brain Tumor Segmentation and Diagnosis Zhao, Liya Jia, Kebin Comput Math Methods Med Research Article 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. Hindawi Publishing Corporation 2016 2016-03-16 /pmc/articles/PMC4812495/ /pubmed/27069501 http://dx.doi.org/10.1155/2016/8356294 Text en Copyright © 2016 L. Zhao and K. Jia. https://creativecommons.org/licenses/by/4.0/ 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. |
spellingShingle | Research Article Zhao, Liya Jia, Kebin Multiscale CNNs for Brain Tumor Segmentation and Diagnosis |
title | Multiscale CNNs for Brain Tumor Segmentation and Diagnosis |
title_full | Multiscale CNNs for Brain Tumor Segmentation and Diagnosis |
title_fullStr | Multiscale CNNs for Brain Tumor Segmentation and Diagnosis |
title_full_unstemmed | Multiscale CNNs for Brain Tumor Segmentation and Diagnosis |
title_short | Multiscale CNNs for Brain Tumor Segmentation and Diagnosis |
title_sort | multiscale cnns for brain tumor segmentation and diagnosis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4812495/ https://www.ncbi.nlm.nih.gov/pubmed/27069501 http://dx.doi.org/10.1155/2016/8356294 |
work_keys_str_mv | AT zhaoliya multiscalecnnsforbraintumorsegmentationanddiagnosis AT jiakebin multiscalecnnsforbraintumorsegmentationanddiagnosis |