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Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images
A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall s...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7665039/ https://www.ncbi.nlm.nih.gov/pubmed/33184301 http://dx.doi.org/10.1038/s41598-020-74419-9 |
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author | Pei, Linmin Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. |
author_facet | Pei, Linmin Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. |
author_sort | Pei, Linmin |
collection | PubMed |
description | A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction using a hybrid method of deep learning and machine learning. To evaluate the performance, we apply the proposed methods to the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) dataset for tumor segmentation and overall survival prediction, and to the dataset of the Computational Precision Medicine Radiology-Pathology (CPM-RadPath) Challenge on Brain Tumor Classification 2019 for tumor classification. We also perform an extensive performance evaluation based on popular evaluation metrics, such as Dice score coefficient, Hausdorff distance at percentile 95 (HD95), classification accuracy, and mean square error. The results suggest that the proposed method offers robust tumor segmentation and survival prediction, respectively. Furthermore, the tumor classification results in this work is ranked at second place in the testing phase of the 2019 CPM-RadPath global challenge. |
format | Online Article Text |
id | pubmed-7665039 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76650392020-11-16 Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images Pei, Linmin Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Sci Rep Article A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction using a hybrid method of deep learning and machine learning. To evaluate the performance, we apply the proposed methods to the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) dataset for tumor segmentation and overall survival prediction, and to the dataset of the Computational Precision Medicine Radiology-Pathology (CPM-RadPath) Challenge on Brain Tumor Classification 2019 for tumor classification. We also perform an extensive performance evaluation based on popular evaluation metrics, such as Dice score coefficient, Hausdorff distance at percentile 95 (HD95), classification accuracy, and mean square error. The results suggest that the proposed method offers robust tumor segmentation and survival prediction, respectively. Furthermore, the tumor classification results in this work is ranked at second place in the testing phase of the 2019 CPM-RadPath global challenge. Nature Publishing Group UK 2020-11-12 /pmc/articles/PMC7665039/ /pubmed/33184301 http://dx.doi.org/10.1038/s41598-020-74419-9 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Pei, Linmin Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images |
title | Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images |
title_full | Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images |
title_fullStr | Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images |
title_full_unstemmed | Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images |
title_short | Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images |
title_sort | context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7665039/ https://www.ncbi.nlm.nih.gov/pubmed/33184301 http://dx.doi.org/10.1038/s41598-020-74419-9 |
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