Brain tumor segmentation is a challenging task necessary for quantitative tumor analysis and diagnosis. We apply a multi-scale convolutional neural network based on the DeepMedic to segment glioma subvolumes provided in the 2018 MICCAI Brain Tumor Segmentation Challenge. We go on to extract intensity and shape features from the images and cross-validate machine learning models to predict overall survival. Using only the mean FLAIR intensity, nonenhancing tumor volume, and patient age we are able to predict patient overall survival with reasonable accuracy.
CITATION STYLE
Gates, E., Pauloski, J. G., Schellingerhout, D., & Fuentes, D. (2019). Glioma segmentation and a simple accurate model for overall survival prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11384 LNCS, pp. 476–484). Springer Verlag. https://doi.org/10.1007/978-3-030-11726-9_42
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