Semi-Automated Segmentation of Glioblastomas in Brain MRI Using Machine Learning Techniques

Abstract

Glioblastomas (GBMs) are cancerous brain tumors that require careful and intricate analysis for surgical planning. Physicians employ Magnetic Resonance Imaging (MRI) in order to diagnose glioblastomas. The segmentation of the tumor is a crucial step in surgical planning. Clinicians manually segment the tumor voxel-by-voxel; however, this is very time consuming. Hence, extensive research has been conducted to semi-automate and fully-automate this segmentation process. This project explores manual segmentation and utilizes k-means clustering technique for semi-automated segmentation. The accuracy of the k-means clustering segmentation was measured using the Dice Coefficient (DC). The results show that k-means clustering provides high accuracy for the segmentation of the enhanced region of tumor (which appears bright in the T1 post contrast MR image) and hence, it can be efficiently used to speed up manual segmentation.