Metadata-Version: 2.1
Name: PYMIC
Version: 0.1
Summary: An open-source deep learning platform for medical image computing
Home-page: https://github.com/ihil/PyMIC
Author: PyMIC Consortium
Author-email: wguotai@gmail.com
License: Apache 2.0
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown

# 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 package is under development. Currently it supports 2D and 3D image segmentation.

# 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). 

# Examples
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.


