Brain Tumor Segmentation from MRI Scans

Project Domain:

Image Processing (Medical Image Analysis)

Introduction

The project aims to develop an automated system for segmenting brain tumors from MRI scans using deep learning techniques. Brain tumor segmentation is a critical task in medical diagnosis and treatment planning, as it helps doctors identify tumor regions accurately and efficiently. With the increasing use of AI in healthcare, this project introduces students to the fundamentals of medical image analysis, deep learning, and computer vision. The system will process MRI scans, detect tumor regions, and provide visual outputs to assist radiologists in diagnosing and treating brain tumors.

Functional Requirements:

1.      Load and Preprocess MRI Data:

Ø  Load multi-modal MRI scans (T1, T1c, T2, FLAIR) from the BraTS dataset.

Ø  Preprocess the data (e.g., normalize pixel values, resize images).

2.      Develop a Segmentation Model:

Ø  Implement a deep learning model (e.g., U-Net) to segment tumor regions from MRI scans.

Ø  Train the model on the BraTS dataset.

3.      Evaluate Model Performance:

Ø  Use metrics like Dice Coefficient, IoU (Intersection over Union), and Accuracy to evaluate the model.

Ø  Visualize segmentation results by overlaying predicted tumor regions on the original MRI scans.

4.      Detect Tumor Sub-Regions:

Ø  Segment different tumor sub-regions (e.g., enhancing tumor, edema, necrotic core).

5.      Create a User Interface:

Ø  Develop a simple graphical interface for doctors to upload MRI scans and view segmentation results.

6.      Logging and Reporting:

Ø  Record segmentation results and generate reports for further analysis.

7.      Documentation:

Ø  Document the system design, deep learning methods, experimental setup, and evaluation results.

Tools & Technologies