Automated Brain Tumor Segmentation on Magnetic Resonance Images and Patient’s Overall Survival Prediction Using Support Vector Machines

This study is aimed to develop two algorithms for glioma tumor segmentation and patient’s overall survival (OS) prediction with machine learning approaches. The segmentation algorithm is fully automated to accurately and efficiently delineate the whole tumor on a magnetic resonance imaging (MRI) scan for radiotherapy treatment planning. The survival algorithm predicts the OS for glioblastoma multiforme (GBM) patients based on regression and classification principles. Multi-institutional BRATS’2017 data of MRI scans from 477 patients with high-grade and lower-grade glioma (HGG/LGG) used in this study. Clinical patient survival data of 291 glioblastoma multiforme (GBM) were available in the provided data. Support vector machines (SVMs) were used to develop both algorithms. The segmentation chain comprises pre-processing with a goal of noise removal, feature extraction of the image intensity, segmentation process using a non-linear classifier with ‘Gaussian’ kernel, and post-processing to enhance the segmentation morphology. The OS prediction algorithm sequence involves two steps; extraction of patient’s age, and segmented tumor’s size and its location features; prediction process using a non-linear classifier and a linear regression model with ‘Gaussian’ kernels. The algorithms were trained, validated and tested on BRATS’2017’s training, validation, and testing datasets. Average Dice for the whole tumor segmentation obtained on the validation and testing datasets is 0.53 ± 0.31 (median 0.60) which indicates the consistency of the proposed algorithm on the new “unseen” data. For OS prediction, the mean accuracy is 0.49 for the validation dataset and 0.35 for the testing dataset based on regression principle; whereas an overall accuracy of 1.00 achieved in classification into short, medium, and long-survivor classes for a designed validation dataset. The computational time for the automated segmentation algorithm took approximately 3 min. In its present form, the segmentation tool is fully automated, fast, and provides a reasonable segmentation accuracy on the multi-institutional dataset.