BraTS18-Challege

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

Contact

• https://github.com/junqiangchen
• email: [email protected]
• Contact: junqiangChen
• WeChat Number: 1207173174
• WeChat Public number: 最新医学影像技术