This is an example of the prostate in transversal T2-weighted MR images Segment from MICCAI Grand Challenge:Prostate MR Image Segmentation 2012
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
1、download trained data,download dataset：https://promise12.grand-challenge.org/download/
2、the file of PROMISE2012Image.csv,is like this format: D:\Data\PROMISE2012\Augmentation\Image/0_1.bmp D:\Data\PROMISE2012\Augmentation\Image/0_10.bmp D:\Data\PROMISE2012\Augmentation\Image/0_2.bmp ...... if you Augmentation trained data path is not D:\Data\PROMISE2012,you should change the csv file path just like this:using C:\Data\ replace D:\Data\PROMISE2012.
3、when data is prepared,just run the vnet_train_predict.py
4、training the model on the GTX1080,it take 20 hours,and i also attach the trained model in the project,you also just use the vnet_train_predict.py file to predict,and get the segmentation result.
5、download trained model:https://pan.baidu.com/s/19E9q6HIUeRB8jpuNhvE2Zg, passworld:obwu
the Challenge result
the loss and model result，the example
- https://github.com/junqiangchen
- email: [email protected]
- WeChat Public number: 最新医学影像技术