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BRaTS stands for Brain Tumor Segmentation. The BRaTS challenge has always been focusing on the evaluation of the state-of-the-art methods for the segmentation of brain tumors in multi-modal magnetic resonance imaging (MRI) scans. This is a coordinated effort for Tumor Segmentation from the University of Pennsylvania, Perelman School of Medicine.
MRI Scans of Glioblastomas/High Grade Glioma (GBM/HGG) and low grade glioma (LGG) with pathologically confirmed diagnosis are labelled and are available for download.
- U-Net
pytorch/models/unet.py
- DeepLab V3 +
tensorflow/models/research/deeplab
File: FLAIR MRI Sequence Data of One Person
File Type: png files
Image-Shape: 240(Slide Width) × 240(Slide Height) × 31(Number of Slide) × 1(Multi-mode)
Image Subjects: 31 persons
- GAD-Enhancing Tumor - WHITE
- Tumor Core - BLACK
- Whole Tumor - GREY
- Background - GREYISH BLACK
- Co-registering
- Interpolation to the same resolution (1 mm^3)
- Skull Stripped
learning rate = 1e-4
maximum number of epochs = 1000
Weights Init: Normal Distribution (mean:0, std:0.01)
Bias Init: Initialized as 0
Step 1: Download complete model and unzip from here.
Step 2:
git clone https://github.com/geekswaroop/BRaTS.git
cd into the downloaded folder and then run
Step 3:
python train.py