GAIR-U-Net: 3D guided attention inception residual u-net for brain tumor segmentation using multimodal MRI images

作者：Evans Kipkoech Rutoh, Qin Zhi Guang, Noor Bahadar, Rehan Raza, Muhammad Shehzad Hanif · 发表于：Journal of King Saud University - Computer and Information Sciences · 年份：2024 · DOI：10.1016/j.jksuci.2024.102086 · 被引用次数：36 · 研究领域：Advanced Neural Network Applications、Brain Tumor Detection and Classification、Medical Image Segmentation Techniques

Deep learning technologies have led to substantial breakthroughs in the field of biomedical image analysis. Accurate brain tumor segmentation is an essential aspect of treatment planning. Radiologists agree that manual segmentation is a difficult and time-consuming task that frequently causes delays in the diagnosing process. While U-Net-based methods have been widely used for brain tumor segmentation, many challenges persist, particularly when dealing with tumors of varying sizes, locations, and shapes. Additionally, segmenting tumor regions with structures requires a comprehensive model, which can increase computational complexity and potentially cause gradient vanishing issues. This study presents a novel method called 3D Guided Attention-based deep Inception Residual U-Net (GAIR-U-Net) to address these challenges. This model combines attention mechanisms, an inception module, and residual blocks with dilated convolution to enhance feature representation and spatial context understanding. The backbone of the model is the U-Net model, which leverages the power of inception and residual connections to capture intricate patterns and hierarchical features while expanding the model’s width in three-dimensional space without significantly increasing computational complexity. The attention mechanisms play a role in focusing on important regions and areas while downgrading irrelevant details. The dilated convolutions in the network help in learning both local and global informatio...