@inproceedings{58876ca625ad45468ccb37d55d315eec,
title = "Glioma segmentation and a simple accurate model for overall survival prediction",
abstract = "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.",
keywords = "Glioblastoma, Neural network, Quantitative imaging, Segmentation",
author = "Evan Gates and Pauloski, {J. Gregory} and Dawid Schellingerhout and David Fuentes",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 4th International MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2018 ; Conference date: 16-09-2018 Through 20-09-2018",
year = "2019",
doi = "10.1007/978-3-030-11726-9_42",
language = "English (US)",
isbn = "9783030117252",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "476--484",
editor = "Farahani Keyvan and Alessandro Crimi and Spyridon Bakas and {van Walsum}, Theo and Mauricio Reyes and Hugo Kuijf",
booktitle = "Brainlesion",
}