Abstract
This paper proposes, in the context of brain tumor study, a fast automatic method that segments tumors and predicts patient overall survival. The segmentation stage is implemented using two fully convolutional networks based on VGG-16, pre-trained on ImageNet for natural image classification, and fine tuned with the training dataset of the MICCAI 2019 BraTS Challenge. The first network yields to a binary segmentation (background vs lesion) and the second one focuses on the enhancing and non-enhancing tumor classes. The final multiclass segmentation is a fusion of the results of these two networks. The prediction stage is implemented using kernel principal component analysis and random forest classifiers. It only requires a predicted segmentation of the tumor and a homemade atlas. Its simplicity allows to train it with very few examples and it can be used after any segmentation process.
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Buatois, T., Puybareau, É., Tochon, G., Chazalon, J. (2020). Two Stages CNN-Based Segmentation of Gliomas, Uncertainty Quantification and Prediction of Overall Patient Survival. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11993. Springer, Cham. https://doi.org/10.1007/978-3-030-46643-5_16
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