Advances in Brain Tumor Segmentation and Skull Stripping: A 3D Residual Attention U-Net Approach

Abstract

The timely diagnosis of brain tumors plays a critical role in enhancing patient prognosis and survival rates. Despite its superior accuracy, manual tumor segmentation is known to be a labor-intensive process. Over the years, a collection of automated tumor segmentation methodologies has been devised and investigated. However, a universally applicable resolution that consistently delivers reliable outcomes across diverse datasets continues to be elusive. Additionally, skull stripping remains a crucial prerequisite to the tumor segmentation procedure. This paper introduces an integrated 3D Attention Residual U-Net (3D_Att_Res_U-Net) model that seamlessly merges attention mechanisms and residual units within the U-Net architecture to augment the performance of brain tumor segmentation and skull stripping in Magnetic Resonance Imaging (MRI). An initial preprocessing stage is implemented, incorporating bias field correction and intensity normalization to optimize performance. The proposed model is trained using the Brain Tumor Segmentation (BraTS) 2020 dataset, along with the Neurofeedback Skull Stripping (NFBS) dataset. The proposed methodology achieved Dice Similarity Coefficients (DSC) of 0.9961 for skull stripping, and 0.9985, 0.9982, and 0.9980 for whole tumor, enhanced tumor, and tumor core segmentation, respectively. Experimental results underscore the applicability and superiority of the proposed approach compared to existing methods in this research domain.