Advanced Brain Tumor Segmentation: A Deep Learning-Based Approach for Augmented Reality Visualization and Interaction in Medical Imaging

Abstract

Brain cancer, marked by abnormal cell growth within the brain, presents a serious threat to individuals due to its high mortality rates. The accuracy of diagnosis and effectiveness of treatment are crucial, requiring prompt detection to improve patient outcomes. However, detecting small tumors is challenging and heavily relies on medical professionals’ expertise, making it prone to errors. Therefore, there is a pressing need for an automated diagnosis system that reduces diagnostic time while improving accuracy. This dissertation focuses on brain tumor segmentation, utilizing deep learning algorithms to tackle the challenges associated with manual diagnosis. While implementing deep learning for tumor segmentation, additional hurdles may arise, such as dealing with low-quality scans, variations in tumor characteristics, and insufficient data. These challenges can be addressed in this work through techniques like data augmentation and synthesis, as well as by selecting suitable segmentation models. Moreover,fostering patient engagement and understanding is essential, alongside training qualified surgeons.Augmented reality emerges as a valuable tool, providing immersive visualization and interactive functionalities to support both diagnosis and surgical interventions.