Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning

1Citations
Citations of this article
25Readers
Mendeley users who have this article in their library.

Abstract

Gliomas are the most common primary brain malignancies. Accurate and robust tumor segmentation and prediction of patients’ overall survival are important for diagnosis, treatment planning and risk factor identification. Here we present a deep learning-based framework for brain tumor segmentation and survival prediction in glioma, using multimodal MRI scans. For tumor segmentation, we use ensembles of three different 3D CNN architectures for robust performance through a majority rule. This approach can effectively reduce model bias and boost performance. For survival prediction, we extract 4,524 radiomic features from segmented tumor regions, then, a decision tree and cross validation are used to select potent features. Finally, a random forest model is trained to predict the overall survival of patients. The 2018 MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), ranks our method at 2nd and 5th place out of 60+ participating teams for survival prediction tasks and segmentation tasks respectively, achieving a promising 61.0% accuracy on the classification of short-survivors, mid-survivors and long-survivors.

Cite

CITATION STYLE

APA

Sun, L., Zhang, S., Chen, H., & Luo, L. (2019). Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning. Frontiers in Neuroscience, 13(JUL). https://doi.org/10.3389/fnins.2019.00810

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free