Brain tumor segmentation is a difficult task due to the strongly varying intensity and shape of gliomas. In this paper we propose a multi-stage discriminative framework for brain tumor segmentation based on BraTSÂ 2018 dataset. The framework presented in this paper is a more complex segmentation system than our previous work presented at BraTS 2016. Here we propose a multi-stage discriminative segmentation model, where every stage is a binary classifier based on the random forest algorithm. Our multi-stage system attempts to follow the layered structure of tumor tissues provided in the annotation protocol. In each segmentation stage we dealt with four major difficulties: feature selection, determination of training database used, optimization of classifier performances and image post-processing. The framework was tested on the evaluation images from BraTS 2018. One of the most important results is the determination of the tumor ROI with a sensitivity of approximately 0.99 in stage I by considering only 16% of the brain in the subsequent stages. Based on the segmentation obtained we solved the survival prediction task using a random forest regressor. The results obtained are comparable to the best ones presented in previous BraTS Challenges.
CITATION STYLE
Lefkovits, S., Szilágyi, L., & Lefkovits, L. (2019). Brain tumor segmentation and survival prediction using a cascade of random forests. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11384 LNCS, pp. 334–345). Springer Verlag. https://doi.org/10.1007/978-3-030-11726-9_30
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