Boosted GLioma Image SegmenTation and Registration
GLISTRboost describes our proposed method for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging (MRI) volumes. Note that this is the winning method of the Multimodal Brain Tumor Image Segmentation (BRATS) Challenge, held in conjunction with the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference in Technische Universitaet Muenchen (TUM) in Munich (Germany) - October 2015.
The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach of a joint segmentation-registration scheme based on an Expectation-Maximization framework, that incorporates a glioma growth model, is used to segment the brain scans into tumor and healthy tissue labels (i.e. GLISTR). Secondly, a discriminative, 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.
Assumptions: All input MRI volumes (i.e. T1, T1-Gad, T2, T2-FLAIR) must be skull-stripped, co-registered and in the LPS coordinate system, for the method to produce meaningful results.
Users should use the visual interface, Cancer and Phenomics Toolkit (CaPTk), to easily make the initializations required by GLISTRboost.
GLISTRboost is currently available through our online Image Processing Portal (IPP) @ ipp.cbica.upenn.edu.
Section of Biomedical Image Analysis (SBIA)
Center for Biomedical Image Computing & Analytics (CBICA)
Department of Radiology
Perelman School of Medicine
University of Pennsylvania
Richards Medical Research Laboratories, Floor 7
3700 Hamilton Walk
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- Spyridon Bakas (1.0.0 - present)
- Ke Zeng (1.0.0 - present)
- Aristeidis Sotiras (1.0.0 - present)
- Saima Rathore (1.0.0 - present)
- Hamed Akbari (1.0.0 - present)
- Martin Rozycki (1.0.0 - present)
- Sarthak Pati (1.0.0 - present)
- George Biros
- Developed the tumor simulator used in GLISTR.
For questions contact: software (at) cbica.upenn.edu
Please cite the following publications when you use GLISTRboost in your research.
- Spyridon Bakas, Ke Zeng, Aristeidis Sotiras, Saima Rathore, Hamed Akbari, Bilwaj Gaonkar, Martin Rozycki, Sarthak Pati, Christos Davatzikos, "GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation", Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer, LNCS, 9556:144-155, 2016. DOI: 10.1007/978-3-319-30858-6_13
(View Publisher's Version)
- Spyridon Bakas, Ke Zeng, Aristeidis Sotiras, Saima Rathore, Hamed Akbari, Bilwaj Gaonkar, Martin Rozycki, Sarthak Pati, Christos Davatzikos, "Segmentation of Gliomas in Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework", In Proceedings of the Multimodal Brain Tumor Image Segmentation Challenge held in conjunction with MICCAI 2015 (MICCAI-BRATS 2015), Technische Universität München (T.U.M.), Munich, Germany, 5-9 Oct 2015