AttUNet : Enhancing ResUNet with Attention Mechanism for Accurate Brain Tumor Segmentation

Brain tumor segmentation is a critical and challenging task in medical image analysis, essential for enhancing patient outcomes through timely detection and treatment. Manual segmentation is both laborious and subjective, highlighting the need for efficient automated solutions. Deep learning, especially models like U-Net, has demonstrated significant potential in biomedical image segmentation. The diversity in brain tumor location, shape, and size adds complexity to automatic segmentation, which involves differentiating abnormal from normal tissues (WM, GM, CSF). Multimodal approaches (e.g., PET/CT, PET/MRI) improve segmentation accuracy by integrating data from various modalities. MRI remains the primary tool for detecting brain tumors, but the process of tumor identification and segmentation is complex and heavily reliant on the expertise of radiologists. Recent advancements in deep learning have markedly enhanced the feasibility of accurate, automated brain tumor segmentation, meeting a critical need in medical imaging. Our proposed model, trained on the TGCA dataset, demonstrated superior performance with DSC 0.94, IoU 0.94 and SI 0.94 compared to traditional models like DeepResnet and Znet. This new method, notable for its performance, success, and pre-processing structure, is highly adaptable for various applications.