Early-Stage Segmentation and Characterization of Brain Tumor

Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain tissues. The life expectancy of patients diagnosed with gliomas decreases exponentially. Most gliomas are diagnosed in later stages, resulting in imminent death. On average, patients do not survive 14 months after diagnosis. The only way to minimize the impact of this inevitable disease is through early diagnosis. The Magnetic Resonance Imaging (MRI) scans, because of their better tissue contrast, are most frequently used to assess the brain tissues. The manual classification of MRI scans takes a reasonable amount of time to classify brain tumors. Besides this, dealing with MRI scans manually is also cumbersome, thus affects the classification accuracy. To eradicate this problem, researchers have come up with automatic and semiautomatic methods that help in the automation of brain tumor classification task. Although, many techniques have been devised to address this issue, the existing methods still struggle to characterize the enhancing region. This is because of low variance in enhancing region which give poor contrast in MRI scans. In this study, we propose a novel deep learning based method consisting of a series of steps, namely: data pre-processing, patch extraction, patch pre-processing, and a deep learning model with tuned hyper-parameters to classify all types of gliomas with a focus on enhancing region. Our trained model achieved better results for all glioma classes including the enhancing region. The improved performance of our technique can be attributed to several factors. Firstly, the non-local mean filter in the pre-processing step, improved the image detail while removing irrelevant noise. Secondly, the architecture we employ can capture the non-linearity of all classes including the enhancing region. Overall, the segmentation scores achieved on the Dice Similarity Coefficient (DSC) metric for normal, necrosis, edema, enhancing and non-enhancing tumor classes are 0.95, 0.97, 0.91, 0.93, 0.95; respectively.

Automated summary

In this study, a novel deep learning-based method is proposed to improve the diagnosis and classification accuracy of gliomas, with a focus on the enhancing region. By combining data preprocessing, patch extraction, patch preprocessing, and a deep learning model, better results were achieved for all types of gliomas, including the enhancing region.