Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas using Deep Learning and MRI

Abstract

Tumor segmentation of magnetic resonance (MR) images is a critical step in providing objective measures of predicting aggressiveness and response to therapy in gliomas. It has valuable applications in diagnosis, monitoring, and treatment planning of brain tumors. The purpose of this work was to develop a fully automated deep learning method for brain tumor segmentation and survival prediction. Well curated brain tumor cases with multi-parametric MR Images from the BraTS2019 dataset were used. A three-group framework was implemented, with each group consisting of three 3D-Dense-UNets to segment whole tumor (WT), tumor core (TC) and enhancing tumor (ET). This method was implemented to decompose the complex multi-class segmentation problem into individual binary segmentation problems for each sub-component. Each group was trained using different approaches and loss functions. The output segmentations of a particular label from their respective networks from the 3 groups were ensembled and post-processed. For survival analysis, a linear regression model based on imaging texture features and wavelet texture features extracted from each of the segmented components was implemented.The networks were tested on the BraTS2019 validation dataset including 125 cases for the brain tumor segmentation task and 29 cases for the survival prediction task. The segmentation networks achieved average dice scores of 0.901, 0.844 and 0.801 for WT, TC and ET respectively. The survival prediction network achieved an accuracy score of 0.55 and mean squared error (MSE) of 119244. This method could be implemented as a robust tool to assist clinicians in primary brain tumor management and follow-up.

Radiomics: from qualitative to quantitative imaging

“…There is active research in combining both deep learning features and radiomics features that shows improved results. [72][73][74] Potential clinical aPPlications Radiomics in oncology Radiomics has been widely studied for application in diagnosis and treatment prognosis/selection in oncology, primarily due to the existence of large imaging data sets used for staging, often containing delineations of tumours and organs at risk necessary for radiation treatment planning. These data sets can be used to train diagnostic and prognostic models for a variety of cancer types and sites.…”

“…103 Deep learning can also be used complementary to traditional handcrafted radiomics studies. For example, studies 72,73 focused on using deep networks for segmentation, followed by radiomics analysis for survival prediction.…”

Multi‐level dilated convolutional neural network for brain tumour segmentation and multi‐view‐based radiomics for overall survival prediction

“…In a recent study, 57 the authors derived imaging, texture, and wavelet imaging features from tumour regions. They combined these features with other volumetric features and fed them to the LR model for the regression.…”

Quantitative imaging analysis

“…While there is no algorithm that can solve every problem, deep learning still has its place and is able to work as additional methods for delineation and feature extraction that compliments handcrafted radiomics. There is active research in combining both deep learning features and radiomics features that shows improved results [72][73][74].…”

“…Deep learning can also be used complementary to traditional hand crafted radiomics studies. For example, studies [72,73] focused on using deep networks for segmentation, followed by radiomics analysis for survival prediction.…”