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BraTS18——Multimodal Brain Tumor Segmentation Challenge 2018

This is an example of the MutiModal MRI images Brain Tumor Segmentation

Prerequisities

The following dependencies are needed:

  • numpy >= 1.11.1
  • SimpleITK >=1.0.1
  • opencv-python >=3.3.0
  • tensorflow-gpu ==1.8.0
  • pandas >=0.20.1
  • scikit-learn >= 0.17.1

How to Use

1、Preprocess

  • analyze the MutiModal MRI image message and Mask image label:run the dataAnaly.py function of getMaskLabelValue() and getImageSizeandSpacing().
  • MutiModal Brain Tumor MRI images have fixed size (240,240,155).
  • generate patch(128,128,64) tumor image and mask for Tumor Segmentation:run the data3dprepare.py.
  • save patch image and mask into csv file: run the utils.py,like file trainSegmentation.csv.
  • split trainSegmentation.csv into training set and test set:run subset.py.

2、Brain Tumor Segmentation

  • the VNet model

  • Tumor Segmentation training:run the train_Brats.py
  • Tumor Segmentation predict:run the predict_Brats.py
  • Tumor Segmentation inference:run the inference_Brats.py

Result

  • the train loss

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