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.
Highlights
A brain tumor is a cancerous or noncancerous mass or growth of abnormal cells in the brain
We present a novel deep learning-based framework for segmentation of a brain tumor and its subregions from multimodal Magnetic resonance imaging (MRI) scans, and survival prediction based on radiomic features extracted from segmented tumor sub-regions as well as clinical features
The result demonstrates that the ensemble model performs better than individual models in enhancing tumor and whole tumor, while CA-convolutional neural networks (CNNs) performs marginally better on the tumor core
Summary
A brain tumor is a cancerous or noncancerous mass or growth of abnormal cells in the brain. Originating in the glial cells, gliomas are the most common brain tumor (Ferlay et al, 2010). Depending on the pathological evaluation of the tumor, gliomas can be categorized into glioblastoma (GBM/HGG), and lower grade glioma (LGG). Glioblastoma is one of the most aggressive and fatal human brain tumors (Bleeker et al, 2012). Gliomas contain various heterogeneous histological sub-regions, including peritumoral edema, a necrotic core, an enhancing and a non-enhancing tumor core. The enhancing tumor sub-region is described by areas that show hyper-intensity in a T1Gd scan when compared to a T1 scan
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