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:
(re)implemented the model with tensorflow in the paper of "Milletari, F., Navab, N., & Ahmadi, S. A. (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation.3DV 2016"
1、download trained data,download dataset：https://promise12.grand-challenge.org/download/ ,if you can't download it,i have shared it:https://pan.baidu.com/s/1y9YAAQKdD3OMOMyamx9MdA, password:whbf
2、the file of promise12Vnet3dImage.csv,is like this format: D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_10 D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_11 D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_12 ...... if you 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 vnet3d_train_predict.py
4、training the model on the GTX1080,it take 40 hours,and i also attach the trained model in the project,you also just use the vnet3d_train_predict.py file to predict,and get the segmentation result.
5、download trained model:https://pan.baidu.com/s/1B869czIPfIL8wxDKgIednQ, password:0nb6
6、download test data: https://pan.baidu.com/s/1pDCQzTxUmyYdwDinBJKTuA, password:s0jt
MICCAI Grand Challenge Result
the trained loss result
the Vnet3D model
the trained process:0 epoch——GTMask and PredictMask
1000 epoch——GTMask and PredictMask
10000 epoch——GTMask and PredictMask
the predict result
