U-Net
A U-Net implementation for brain tumor segmentation using the BRATS 2017 dataset with data augmentation and dice loss.
What is U-Net?
U-Net Brain Tumor Segmentation is a deep learning implementation for automatically segmenting brain tumors from MRI scans using the U-Net architecture. It processes the BRATS 2017 dataset containing multi-modal MRI volumes and trains models to identify different tumor regions including necrotic tissue, edema, and enhancing tumors. The project provides complete training pipelines with data augmentation and evaluation metrics specifically designed for medical image segmentation tasks.
Researchers and developers working on medical image analysis, particularly those focused on brain tumor segmentation using deep learning. It's also suitable for students learning about U-Net implementations for biomedical applications.
This implementation offers a practical, well-documented example of brain tumor segmentation with advanced data augmentation techniques like elastic transformation. It provides ready-to-use training scripts and demonstrates how to handle the specific challenges of medical imaging datasets compared to general computer vision projects.
Overview
U-Net Brain Tumor Segmentation
Use Cases
Best For
- Learning U-Net implementations for medical image segmentation
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