Metadata-Version: 2.1
Name: PYMIC
Version: 0.2
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
Description: # 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
        
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
