Abstract
Identification and localization of brain tumor tissues plays an important role in diagnosis and treatment planning of gliomas. A fully automated superpixel wise two-stage tumor tissue segmentation algorithm using random forest is proposed in this paper. First stage is used to identify total tumor and the second stage to segment sub-regions. Features for random forest classifier are extracted by constructing a tensor from multimodal MRI data and applying multi-linear singular value decomposition. The proposed method is tested on BRATS 2017 validation and test dataset. The first stage model has a Dice score of 83% for the whole tumor on the validation dataset. The total model achieves a performance of 77%, 50% and 61% Dice scores for whole tumor, enhancing tumor and tumor core, respectively on the test dataset.
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Acknowledgment
This research was supported by: Flemish Government FWO project G.0869.12N (Tumor imaging), G.0830.14N (Block term decompositions); IWT IM 135005; imec funds 2017; imec ICON project: ICON HBC.2016.0167, ‘SeizeIT’, #316679 and ERC Advanced Grant. The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC Advanced Grant: BIOTENSORS (no 339804). This paper reflects only the author’s views and the Union is not liable for any use that may be made of the contained information.
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Bharath, H.N., Colleman, S., Sima, D.M., Van Huffel, S. (2018). Tumor Segmentation from Multimodal MRI Using Random Forest with Superpixel and Tensor Based Feature Extraction. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science(), vol 10670. Springer, Cham. https://doi.org/10.1007/978-3-319-75238-9_39
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