Automatic Segmentation and Shape, Texture-based Analysis of Glioma Using Fully Convolutional Network

Lower-grade glioma is a type of brain tumor that is usually found in the human brain and spinal cord. Early detection and accurate diagnosis of lower-grade glioma can reduce the fatal risk of the affected patients. An essential step for lower-grade glioma analysis is MRI Image Segmentation. Manual segmentation processes are time-consuming and depend on the expertise of the pathologist. In this study, three different deep-learning-based automatic segmentation models were used to segment the tumor-affected region from the MRI slice. The segmentation accuracy of the three models-U-Net, FCN, and U-Net with ResNeXt50 backbone were respectively 80%, 84%, and 91%. Two shape-based features- (angular standard deviation, marginal fluctuation) and six texture-based features (entropy, local binary pattern, homogeneity, contrast, correlation, energy) were extracted from the segmented images to find the association with seven existing genomic data types. It was found out that there was a significant association between the genomic data type-microRNA cluster and texture-based feature-entropy case and genomic data type-RNA sequence cluster with shape-based feature-angular standard deviation case. In both of these cases, the p values were observed less than 0.05 for the Fisher exact test.