Two-phase MRI brain tumor segmentation using random forests and level set methods

Abstract

Magnetic resonance images (MRI) in various modalities contain valuable information usable in medical diagnosis. Accurate delimitation of the brain tumor and its internal tissue structures is very important for the evaluation of disease progression, for studying the effects of a chosen treatment strategy and for surgical planning as well. At the same time early detection of brain tumors and the determination of their nature have long been desirable in preventive medicine. The goal of this study is to develop an intelligent software tool for quick detection and accurate segmentation of brain tumors from MR images. In this paper we describe the developed two-staged image segmentation framework. The first stage is a voxelwise classifier based on random forest (RF) algorithm. The second acquires the accurate boundaries by evolving active contours based on the level set method (LSM). The intelligent combination of two powerful segmentation algorithms ensures performances that cannot be achieved by either of these methods alone. In our work we used the MRI database created for the BraTS ’14-‘16 challenges, considered a gold standard in brain tumor segmentation. The segmentation results are compared with the winning state of the art methods presented at the Brain Tumor Segmentation Grand Challenge and Workshop (BratsTS).