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tbhswanand/
brats-mri-segmentation

Name: brats-mri-segmentation
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🧠 BraTS MRI Tumor Segmentation (UNet-based)

📌 Overview

This model performs brain tumor segmentation on MRI scans using a deep learning architecture (UNet-based). It is trained on the BraTS dataset for segmenting tumor regions such as:

Enhancing Tumor (ET)
Tumor Core (TC)
Whole Tumor (WT)

🧪 Model Details

Architecture: UNet / UNet-like
Framework: PyTorch
Input: MRI slices (multi-modal or single modality)
Output: Segmentation mask
Task: Medical Image Segmentation

📂 Dataset

Dataset: BraTS (Brain Tumor Segmentation Challenge)
Modalities: T1, T1c, T2, FLAIR
Preprocessing:
- Normalization
- Resizing
- Skull stripping (if applied)

⚙️ Usage

🔹 Install dependencies

pip install torch torchvision nibabel numpy

🔹 Load model

python

import torch

model = torch.load("model.pth", map_location="cpu")
model.eval()

🔹 Run inference

python

import numpy as np

# Example input (replace with actual preprocessing)
input_tensor = torch.randn(1, 4, 240, 240)

with torch.no_grad():
    output = model(input_tensor)

mask = output.argmax(dim=1)

📊 Performance

Metric	Score
Dice Score (WT)	~0.85
Dice Score (TC)	~0.80
Dice Score (ET)	~0.75

Note: Values may vary based on training setup

🚀 Applications

Clinical decision support
Tumor monitoring
Radiology AI systems
Medical research

⚠️ Limitations

Not for direct clinical use
Performance depends on preprocessing
Requires GPU for fast inference

🔐 License

Specify your license here (MIT / Apache 2.0 / etc.)

👨‍💻 Author

Swanand Wakadmane AI/ML Developer | Medical AI Enthusiast

🤝 Acknowledgements

BraTS Challenge
Medical imaging research community

tbhswanand/brats-mri-segmentation