Deep Learning-Based Multimodal MRI Brain Tumor Image Segmentation

Professor Introduction

J. W | Ph.D. in Computer Science

Home Institute：East China University of Science and Technology

Project Description

This project aims to explore the integration of complementary information from different modalities of MRI brain tumor images to enhance tumor segmentation. The goal is to provide scientific methods and advanced tools for screening, diagnosing, and treating brain tumors in clinical medicine, thereby reducing doctors' workload and improving their efficiency. Through this project, students will be exposed to state-of-the-art deep learning and computer vision technologies and learn how to apply these technologies to solve real-world problems. More importantly, students will enhance their ability to identify, analyze, and solve research problems.

Project Keywords

Project Outline

Part 1 : Introduction to Medical Imaging and Brain Tumors
• Overview of Medical Imaging Technologies (CT, MRI, PET, Ultrasound)
• Importance of Medical Imaging in Disease Screening, Diagnosis, and Treatment
• Introduction to Brain Tumors and Their Clinical Significance

Part 2 : Research Objectives and Hypotheses
• Investigating the Enhancement of Tumor Segmentation by Integrating Multimodal MRI Images
• Developing and Validating Deep Learning Models for Tumor Segmentation

Part 3 :  Review of Current Research and Methods
• Review of Existing Methods for Brain Tumor Segmentation
• Identification of Gaps and Limitations in Current Technologies

Part 4:  Data Collection and Preprocessing
• Collection of Multimodal MRI Brain Tumor Datasets
• Preprocessing Techniques for Image Enhancement and Normalization
• Data Augmentation Strategies to Improve Model Robustness

Part 5 :  Development of Deep Learning Models
• Building Convolutional Neural Networks (CNNs) for Tumor Segmentation
• Incorporating Multimodal Data into the CNN Architecture
• Setting Up Training Parameters and Loss Functions

Part 6 :  Model Training and Evaluation
• Training the Deep Learning Models on Multimodal MRI Data
• Evaluating Model Performance Using Metrics such as Dice Coefficient and IoU
• Fine-Tuning Hyperparameters to Optimize Model Accuracy

Part 7: Comparative Analysis and Model Optimization
• Iteratively Optimizing the Model for Better Performance
• Comparing the Performance of Different Deep Learning Architectures
• Analyzing the Impact of Multimodal Integration on Segmentation Accuracy

Part 8: Results and Discussion
• Graphical Representation of Segmentation Results and Performance Metrics
• Interpretation of Results and Discussion of Clinical Implications
• Comparison with Existing Segmentation Methods and Discussion of Advantages and Limitations

Part 9: Conclusion and Future Directions
• Summary of Key Findings and Their Significance
• Identification of Research Limitations and Suggestions for Future Research
• Recommendations for Practical Applications in Clinical Medicine

Part 10: Reporting and Presentation
• Writing a Detailed Research Report with Clear Structure, Concise Language, and Accurate Data Presentation
• Preparing and Delivering a Clear and Engaging Oral Presentation of Research Background, Methods, Results, and Conclusions

Suitable for

High School Students:  
• Interest in AI and Medical Imaging: Students with a strong interest in artificial intelligence, machine learning, and medical imaging technologies.
• Basic Knowledge : Students with a basic understanding of programming and image processing concepts.