# PyMIC

**Repository Path**: yeqiuyi/PyMIC

## Basic Information

- **Project Name**: PyMIC
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-07-06
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PyMIC: A Pytorch-Based Toolkit for Medical Image Computing

This repository proivdes a library and some examples of using pytorch for medical image computing. The toolkit is under development. Currently it supports 2D and 3D image segmentation. It was originally developped for COVID-19 pneumonia lesion segmentation from CT images. If you use this toolkit, please cite the following paper:

*  G. Wang, X. Liu, C. Li, Z. Xu, J. Ruan, H. Zhu, T. Meng, K. Li, N. Huang, S. Zhang. 
[A Noise-robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions from CT Images.][tmi2020] IEEE Transactions on Medical Imaging. 2020. DOI: [10.1109/TMI.2020.3000314][tmi2020]

[tmi2020]:https://ieeexplore.ieee.org/document/9109297

# Requirement
* [Pytorch][torch_link] version >=1.0.1
* [TensorboardX][tbx_link] to visualize training performance
* Some common python packages such as Numpy, Pandas, SimpleITK

[torch_link]:https://pytorch.org/
[tbx_link]:https://github.com/lanpa/tensorboardX 

# Advantages
This package provides some basic modules for medical image computing that can be share by different applications. We currently provide the following functions:
* Easy-to-use I/O interface to read and write different 2D and 3D images.
* Re-userable training and testing pipeline that can be transfered to different tasks.
* Various data pre-processing methods before sending a tensor into a network.
* Implementation of loss functions (for image segmentation).
* Implementation of evaluation metrics to get quantitative evaluation of your methods (for segmentation). 

# Usage
Run the following command to install PyMIC:

```bash
pip install PYMIC
```

Go to `examples` to see some examples for using PyMIC. For beginners, you only need to simply change the configuration files to select different datasets, networks and training methods for running the code (example 1 - 3). For advanced users, you can develop your own modules based on this package (example 4). You can find the following examples:

1, `examples\JSRT`: use a predefined 2D U-Net for heart segmentation from X-ray images.

2, `examples\fetal_hc`: use a predefined 2D U-Net for fetal brain segmentation from ultrasound images.

3, `examples\prostate`: use a predefined 3D U-Net for prostate segmentation from 3D MRI.

4, `examples\JSRT2`: define a network by yourself for heart segmentation from X-ray images.

# Projects based on PyMIC
Using PyMIC, it becomes easy to develop deep learning models for different projects, such as the following:

1, [COPLE-Net][coplenet] COVID-19 Pneumonia Segmentation from CT images. 

2, [Head-Neck-GTV][hn_gtv] Nasopharyngeal Carcinoma (NPC) GTV segmentation from Head and Neck CT images. 

[coplenet]:https://github.com/HiLab-git/COPLE-Net
[hn_gtv]: https://github.com/HiLab-git/Head-Neck-GTV