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. 2016:9556:144-155.
doi: 10.1007/978-3-319-30858-6_1.

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

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
Affiliations

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

Spyridon Bakas et al. Brainlesion. 2016.

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

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Figures

Fig. 1
Fig. 1
Example of using a single seed-point and a radius to approximate the center and the bulk volume of a tumor by a sphere. The figures illustrate (from left to right) the axial, coronal and sagittal view of the same patient.
Fig. 2
Fig. 2
Examples for four LGG and four HGG patients. Green, red and blue masks denote the edema, the enhancing tumor and the union of the necrotic and non-enhancing parts of the tumor, respectively (Color figure online).
Fig. 3
Fig. 3
Distributions of the DICE score across patients for each step (G: GLISTR, GB: gradient boosting, P: proposed) of the proposed method, each tissue label and different groupings of data. The black cross and the red line inside each box denote the mean and median values, respectively (Color figure online).
See this image and copyright information in PMC

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