Brain Tumor Segmentation with Deep Learning

This project focuses on developing advanced deep learning techniques for automated brain tumor segmentation in medical imaging. By incorporating hemodynamic properties into the segmentation process, we achieve improved accuracy in identifying tumor boundaries and characteristics.

Project Overview

Brain tumor segmentation is a critical task in medical imaging that directly impacts treatment planning and patient outcomes. Traditional segmentation methods often struggle with the complex and heterogeneous nature of brain tumors. This project addresses these challenges by:

• Integrating hemodynamic data with traditional imaging modalities
• Developing novel deep learning architectures optimized for medical image analysis
• Improving segmentation accuracy for better clinical decision-making

Key Contributions

• Novel incorporation of hemodynamic properties in deep learning models
• Enhanced segmentation performance on challenging brain tumor cases
• Potential for clinical translation and improved patient care

Research Impact

This work contributes to the growing field of AI-assisted medical imaging and has potential applications in:

• Clinical diagnosis: More accurate tumor boundary detection
• Treatment planning: Better visualization for surgical and radiation therapy
• Research: Enhanced understanding of tumor hemodynamics

The research has been published in Quantitative Imaging in Medicine & Surgery and represents a significant step forward in applying artificial intelligence to medical imaging challenges.