CBSN: Comparative measures of normalization techniques for brain tumor segmentation using SRCNet

Abstract

The segmentation of tumors in the brain MRI scans is a difficult job for doctors and radiologists. The segmentation done by different medical experts may also have differences in their opinion for the segmented region, which is popularly known as regions of interest (ROIs). To date, researchers and academicians have proposed several approaches and frameworks for semi- and full-automatic segmentation techniques to identify ROIs accurately. It is prevalent that automatic segmentation gives comparable or even better results compared to human experts for several publicly known and privately collected datasets. Additionally, these are beneficial in those areas where doctors and radiologists’ availability is either uneven or scarce because of geographical dispersion. The convolutional neural networks (CNN) are considered for segmentation for ROIs due to their wide popularity. They have outperformed humans over tasks like object identification and image classification. The publicly available datasets or those collected from different medical institutions may have different statistics, resolution, and properties. Therefore, pre-processing has an essential role in achieving better and accurate delineation and segmentation of tumors. In the proposed work, CBSN, we consider well-known normalization techniques such as Gaussian Mixture Models (GMM), Fuzzy C-Means (FCM), and Z-score normalization for pre-processing the BraTS (Brain Tumor Segmentation Challenge) 2018 dataset. We utilized three variants of U-Net architecture, convolutional block attention module (CBAM), squeeze and excitation module (SEM), and refinement module (RM) for the segmentation of the ROIs. Utilizing Z-score performs better than other normalization techniques for tumor core (TC) and whole tumor (WT) segmentation. In contrast, FCM performs superior to the other two normalization techniques on enhancement tumor (ET) segmentation.