/BraTS18-Challege

Multimodal Brain Tumor Segmentation Challenge 2018

Primary LanguagePython

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 inference:run the predict_Brats.py.

Result

  • the train loss

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