We developed an advanced 3D U-Net deep learning model deployed through a FastAPI backend to automatically segment brain tumor regions from MRI scans. The solution was trained using the MONAI medical imaging framework, enabling high precision and medical-grade reliability.
The model achieved a 95% Dice score, delivering near human-level performance.

Our Approach

To ensure accuracy, speed, and real-world usability, our approach involved:

1. Data Preparation & Preprocessing

• MRI datasets cleaned and standardized

• Augmentation applied for robustness

• Processed using OpenCV and MONAI transforms

2. Model Development

• Built a 3D U-Net architecture optimized for medical imaging

• Implemented using Python, PyTorch, and MONAI

• Trained on multi-modal MRI scans

• Validated with Dice Similarity Coefficient (DSC)

  • 95% DSC means the model’s segmented tumor region matches the expert’s segmentation by 95%.

3. Deployment for Real Use

• Served via FastAPI for fast inference

• Designed for clinic-ready performance

• Outputs segmentation masks in under 1 second

Tech Stack:

Python • PyTorch • MONAI • 3D U-Net • OpenCV • FastAPI

Challenges to Overcome

• High variability in MRI scans due to different scanners and patient conditions

• Time-consuming manual segmentation (15+ minutes per scan)

• Need for real-time results during neurosurgical planning

• Limited annotated data for training medical AI models

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