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Zeng K, Bakas S, Sotiras A, Akbari H, Rozycki M, Rathore S, Pati S, Davatzikos C. Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework. Brainlesion 2016; 10154:184-194. [PMID: 28725878 PMCID: PMC5512606 DOI: 10.1007/978-3-319-55524-9_18] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We present an approach for segmenting both low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed framework is an extension of our previous work [6,7], with an additional component for segmenting post-operative scans. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative model based on a joint segmentation-registration framework is used to segment the brain scans into cancerous and healthy tissues. Secondly, a gradient boosting classification scheme is used to refine tumor segmentation based on information from multiple patients. We evaluated our approach in 218 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2016 challenge and report promising results. During the testing phase, the proposed approach was ranked among the top performing methods, after being additionally evaluated in 191 unseen cases.
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Affiliation(s)
- Ke Zeng
- Section of Biomedical Image Analysis, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Spyridon Bakas
- Section of Biomedical Image Analysis, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Aristeidis Sotiras
- Section of Biomedical Image Analysis, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Hamed Akbari
- Section of Biomedical Image Analysis, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Martin Rozycki
- Section of Biomedical Image Analysis, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Saima Rathore
- Section of Biomedical Image Analysis, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Sarthak Pati
- Section of Biomedical Image Analysis, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Christos Davatzikos
- Section of Biomedical Image Analysis, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
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Bakas S, Zeng K, Sotiras A, Rathore S, Akbari H, Gaonkar B, Rozycki M, Pati S, Davatzikos C. GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation. Brainlesion 2016; 9556:144-55. [PMID: 28725877 DOI: 10.1007/978-3-319-30858-6_1] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
We present an approach for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification scheme is used to refine tumor labels based on information from multiple patients. Lastly, a probabilistic Bayesian strategy is employed to further refine and finalize the tumor segmentation based on patient-specific intensity statistics from the multiple modalities. We evaluated our approach in 186 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.
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