A Glioma Segmentation Method Using CoTraining and Superpixel-Based Spatial and Clinical Constraints

Various brain tumors are very harmful to human health, and their incidence rate has risen gradually in recent years. Magnetic resonance imaging is widely used in the evaluation and treatment of brain tumors, but a radiologist's manual segmentation of massive images is time-consuming and produces some personal bias, and so a computer-aided diagnostic system could improve the working efficiency of radiologists. In recent years, a variety of automatic segmentation methods for brain tumor images has been proposed. Compared with other methods, machine-learning-based methods are able to obtain better segmentation accuracy, but such methods require the manual labeling of lots of samples to ensure the accuracy of the segmentation. In this study, semi-supervised learning theory and image spatial and clinical a priori knowledge of brain tumors are combined to propose a new brain-tumor segmentation method that can improve the segmentation accuracy with multiple classifier collaborative training (CoTraining) under the premise of fewer labeled data. In addition, according to the prior knowledge that image adjacent pixels belong to similar classes and clinical knowledge, we used superpixel graphs to construct a spatial and clinical constraint to improve brain-tumor segmentation accuracy further. The proposed method was tested on the Brain Tumor Segmentation Challenge 2012 and 2013 datasets (BRATS 2012, 2013). The experimental results demonstrate that the proposed method was able to overcome the drawbacks of fewer training samples effectively and obtained remarkable brain-tumor segmentation performance.