# BraTS18——Multimodal Brain Tumor Segmentation Challenge 2018

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

![](BRATS_banner_noCaption.png)

## 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

![](3dVNet.png) 

* 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

![](loss.PNG)

![](result.PNG)

## Contact

* https://github.com/junqiangchen

* email: 1207173174@qq.com

* Contact: junqiangChen

* WeChat Number: 1207173174

* WeChat Public number: 最新医学影像技术