The semantic segmentation of a brain tumor is of paramount importance for its treatment and prevention. Recently, researches have proposed various neural network-based architectures to improve the performance of segmentation of brain tumor sub-regions. Brain tumor segmentation, being a challenging area of research, requires improvement in its performance. This paper proposes a 2D image segmentation method, BU-Net, to contribute to brain tumor segmentation research. Residual extended skip (RES) and wide context (WC) are used along with the customized loss function in the baseline U-Net architecture. The modifications contribute by finding more diverse features, by increasing the valid receptive field. The contextual information is extracted with the aggregating features to get better segmentation performance. The proposed BU-Net was evaluated on the high-grade glioma (HGG) datasets of the BraTS2017 Challenge—the test datasets of the BraTS 2017 and 2018 Challenge datasets. Three major labels to segmented were tumor core (TC), whole tumor (WT), and enhancing core (EC). To compare the performance quantitatively, the dice score was utilized. The proposed BU-Net outperformed the existing state-of-the-art techniques. The high performing BU-Net can have a great contribution to researchers from the field of bioinformatics and medicine.
MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks
