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+# 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