# VNet3D

**Repository Path**: eyelvex/VNet3D

## Basic Information

- **Project Name**: VNet3D
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-06-04
- **Last Updated**: 2025-06-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ImageSegmentation With Vnet3D
> This is an example of the prostate in transversal T2-weighted MR images Segment from MICCAI Grand Challenge:Prostate MR Image Segmentation 2012
![](promise12_header.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
(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/1kQ1SCVuBK6xJFR7cyKN7XQ password:0ytv**

**6、download test data: https://pan.baidu.com/s/1pDCQzTxUmyYdwDinBJKTuA, password:s0jt**

## Result
MICCAI Grand Challenge Result
![](leadboard9.PNG)

the trained loss result
![](loss.PNG)
the Vnet3D model
![](vnet.PNG)
the trained process:0 epoch——GTMask and PredictMask
![](gt_0_epoch.png)
![](predict_0_epoch.png)
1000 epoch——GTMask and PredictMask
![](gt_1000_epoch.png)
![](predict_1000_epoch.png)
10000 epoch——GTMask and PredictMask
![](gt_10000_epoch.png)
![](predict_10000_epoch.png)
the predict result
![](mask_15_epoch.png)
![](src_15_epoch.png)
## Contact
* https://github.com/junqiangchen
* email: 1207173174@qq.com
* WeChat Public number: 最新医学影像技术