# H-DenseUNet

**Repository Path**: huogenwang/H-DenseUNet

## Basic Information

- **Project Name**: H-DenseUNet
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-11-29
- **Last Updated**: 2021-11-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes, TMI 2018. 
by [Xiaomeng Li](https://scholar.google.com/citations?user=uVTzPpoAAAAJ&hl=en), [Hao Chen](http://appsrv.cse.cuhk.edu.hk/~hchen/), [Xiaojuan Qi](https://xjqi.github.io/), [Qi Dou](http://appsrv.cse.cuhk.edu.hk/~qdou/), [Chi-Wing Fu](http://www.cse.cuhk.edu.hk/~cwfu/), [Pheng-Ann Heng](http://www.cse.cuhk.edu.hk/~pheng/). 

### Introduction

This repository is for our TMI 2018 paper '[H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes](http://arxiv.org/pdf/1709.07330.pdf)'.

### Environment:
	This code is only tested under python2. Check code environment "requirements.txt"


### Usage


1. Data preprocessing: 
   Download dataset from: [Liver Tumor Segmentation Challenge](https://drive.google.com/drive/folders/0B0vscETPGI1-Q1h1WFdEM2FHSUE).   
   Then put 131 training data with segmentation masks under "data/TrainingData/" and 70 test data under "data/TestData/".  
   Run:
   ```shell 
   python preprocessing.py 
   ```


2. Test our model:
   Download liver mask from [LiverMask](https://drive.google.com/file/d/14HxHiOKcJtpbOOvPqx-4XN7_Jrdy1Fby/view?usp=sharing) and put them in the folder: 'livermask'.   
   Download model from [Model](https://drive.google.com/file/d/1Qo4TFR4hf5wVPJSkMqGMEf4O4GjRHRyU/view?usp=sharing) and put them in the folder: 'model'.
   run:
   ```shell
   python test.py
   ```

3. Train 2D DenseUnet:
    First, you need to download the pretrained model from [ImageNet Pretrained](https://drive.google.com/file/d/1HHiPBKPw539LR0Oj5g1gD3FNRkCsxeGi/view?usp=sharing), extract it and put it in the folder 'model'.
    Then run:
   ```shell
   sh bash_train.sh
   ```

4. Train H-DenseUnet:
    Load your trained model and run   
    
   ```shell
   CUDA_VISIBLE_DEVICES='0' python train_hybrid.py -arch 3dpart
   ```

5. Train H-DenseUnet in end-to-end way:
    
   ```shell
   CUDA_VISIBLE_DEVICES='0' python train_hybrid.py -arch end2end
   ```


## Citation

If H-DenseUNet is useful for your research, please consider citing:

  ```shell
  @article{li2018h,
  title={H-denseunet: Hybrid densely connected unet for liver and tumor segmentation from ct volumes},
  author={Li, Xiaomeng and Chen, Hao and Qi, Xiaojuan and Dou, Qi and Fu, Chi-Wing and Heng, Pheng-Ann},
  journal={IEEE transactions on medical imaging},
  volume={37},
  number={12},
  pages={2663--2674},
  year={2018},
  publisher={IEEE}
  }

  ```


### Questions

Please contact 'xmli@cse.cuhk.edu.hk'