Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images

Abstract

Gliomas are the most common malignant brain tumors with different grades that highly determine the rate of survival in patients. Tumor segmentation and grading using magnetic resonance imaging (MRI) are common and essential for diagnosis and treatment planning. To achieve this clinical need, a deep learning approach that combines convolutional neural networks (CNN) based on the U-net for tumor segmentation and transfer learning based on a pre-trained convolution-base of Vgg16 and a fully connected classifier for tumor grading was developed. The segmentation and grading models use the same pipeline of T1-precontrast, fluid attenuated inversion recovery (FLAIR), and T1-postcontrast MRI images of 110 patients of lower-grade glioma (LGG) for training and evaluations. The mean dice similarity coefficient (DSC) and tumor detection accuracy achieved by the segmentation model are 0.84 and 0.92, respectively. The grading model classifies LGG into grade II and grade III with accuracy, sensitivity, and specificity of 0.89, 0.87, and 0.92, respectively at the MRI images' level and 0.95, 0.97, and 0.98 at the patients’ level. This work demonstrates the potential of using deep learning in MRI images to provide a non-invasive tool for simultaneous and automated tumor segmentation, detection, and grading of LGG for clinical applications.