Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning

Frontiers in Neuroscience

Lin Luo¹  Songtao Zhang²  Li Sun²  Hang Chen² 

[1] College of Engineering, Peking University, Beijing, China;School of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen, China;

关键词: survival prediction;    brain tumor segmentation;    3D CNN;    multimodal MRI;    deep learning;   

DOI  :  10.3389/fnins.2019.00810

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown