GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation

  • Spyridon Bakas
  • Ke Zeng
  • Aristeidis Sotiras
  • Saima Rathore
  • Hamed Akbari
  • Bilwaj Gaonkar
  • Martin Rozycki
  • Sarthak Pati
  • Christos Davatzikos
Conference paper

DOI: 10.1007/978-3-319-30858-6_13

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9556)
Cite this paper as:
Bakas S. et al. (2016) GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation. In: Crimi A., Menze B., Maier O., Reyes M., Handels H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science, vol 9556. Springer, Cham

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.

Keywords

Segmentation Brain tumor Glioma Multimodal MRI BRATS challenge Gradient boosting Expectation maximization Brain tumor growth model Probabilistic model 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Spyridon Bakas
    • 1
  • Ke Zeng
    • 1
  • Aristeidis Sotiras
    • 1
  • Saima Rathore
    • 1
  • Hamed Akbari
    • 1
  • Bilwaj Gaonkar
    • 1
  • Martin Rozycki
    • 1
  • Sarthak Pati
    • 1
  • Christos Davatzikos
    • 1
  1. 1.Section of Biomedical Image Analysis, Center for Biomedical Image Computing and Analytics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA

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