Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features
Accurate segmentation of different sub-regions of gliomas such as peritumoral edema, necrotic core, enhancing, and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appea...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-04-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fncom.2020.00025/full |
| _version_ | 1818851528468856832 |
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| author | Xue Feng Nicholas J. Tustison Sohil H. Patel Craig H. Meyer Craig H. Meyer |
| author_facet | Xue Feng Nicholas J. Tustison Sohil H. Patel Craig H. Meyer Craig H. Meyer |
| author_sort | Xue Feng |
| collection | DOAJ |
| description | Accurate segmentation of different sub-regions of gliomas such as peritumoral edema, necrotic core, enhancing, and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape of these tumors, segmentation of the sub-regions is challenging. Recent developments using deep learning models has proved its effectiveness in various semantic and medical image segmentation tasks, many of which are based on the U-Net network structure with symmetric encoding and decoding paths for end-to-end segmentation due to its high efficiency and good performance. In brain tumor segmentation, the 3D nature of multimodal MRI poses challenges such as memory and computation limitations and class imbalance when directly adopting the U-Net structure. In this study we aim to develop a deep learning model using a 3D U-Net with adaptations in the training and testing strategies, network structures, and model parameters for brain tumor segmentation. Furthermore, instead of picking one best model, an ensemble of multiple models trained with different hyper-parameters are used to reduce random errors from each model and yield improved performance. Preliminary results demonstrate the effectiveness of this method and achieved the 9th place in the very competitive 2018 Multimodal Brain Tumor Segmentation (BraTS) challenge. In addition, to emphasize the clinical value of the developed segmentation method, a linear model based on the radiomics features extracted from segmentation and other clinical features are developed to predict patient overall survival. Evaluation of these innovations shows high prediction accuracy in both low-grade glioma and glioblastoma patients, which achieved the 1st place in the 2018 BraTS challenge. |
| first_indexed | 2024-12-19T07:06:28Z |
| format | Article |
| id | doaj.art-d9d881321ab44ca98569cde5438228ea |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-19T07:06:28Z |
| publishDate | 2020-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-d9d881321ab44ca98569cde5438228ea2022-12-21T20:31:17ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882020-04-011410.3389/fncom.2020.00025488825Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic FeaturesXue Feng0Nicholas J. Tustison1Sohil H. Patel2Craig H. Meyer3Craig H. Meyer4Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United StatesDepartment of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United StatesDepartment of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United StatesDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, United StatesDepartment of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United StatesAccurate segmentation of different sub-regions of gliomas such as peritumoral edema, necrotic core, enhancing, and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape of these tumors, segmentation of the sub-regions is challenging. Recent developments using deep learning models has proved its effectiveness in various semantic and medical image segmentation tasks, many of which are based on the U-Net network structure with symmetric encoding and decoding paths for end-to-end segmentation due to its high efficiency and good performance. In brain tumor segmentation, the 3D nature of multimodal MRI poses challenges such as memory and computation limitations and class imbalance when directly adopting the U-Net structure. In this study we aim to develop a deep learning model using a 3D U-Net with adaptations in the training and testing strategies, network structures, and model parameters for brain tumor segmentation. Furthermore, instead of picking one best model, an ensemble of multiple models trained with different hyper-parameters are used to reduce random errors from each model and yield improved performance. Preliminary results demonstrate the effectiveness of this method and achieved the 9th place in the very competitive 2018 Multimodal Brain Tumor Segmentation (BraTS) challenge. In addition, to emphasize the clinical value of the developed segmentation method, a linear model based on the radiomics features extracted from segmentation and other clinical features are developed to predict patient overall survival. Evaluation of these innovations shows high prediction accuracy in both low-grade glioma and glioblastoma patients, which achieved the 1st place in the 2018 BraTS challenge.https://www.frontiersin.org/article/10.3389/fncom.2020.00025/fullbrain tumor segmentationensemble3D U-netdeep learningsurvival predictionlinear regression |
| spellingShingle | Xue Feng Nicholas J. Tustison Sohil H. Patel Craig H. Meyer Craig H. Meyer Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features Frontiers in Computational Neuroscience brain tumor segmentation ensemble 3D U-net deep learning survival prediction linear regression |
| title | Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features |
| title_full | Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features |
| title_fullStr | Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features |
| title_full_unstemmed | Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features |
| title_short | Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features |
| title_sort | brain tumor segmentation using an ensemble of 3d u nets and overall survival prediction using radiomic features |
| topic | brain tumor segmentation ensemble 3D U-net deep learning survival prediction linear regression |
| url | https://www.frontiersin.org/article/10.3389/fncom.2020.00025/full |
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