Metadata-Version: 2.1
Name: miscnn
Version: 1.2.0
Summary: Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
Home-page: https://github.com/frankkramer-lab/MIScnn
Author: Dominik Müller
Author-email: dominik.mueller@informatik.uni-augsburg.de
License: GPLv3
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: tensorflow (==2.5.0)
Requires-Dist: tensorflow-addons (==0.13.0)
Requires-Dist: numpy (==1.19.2)
Requires-Dist: pandas (>=1.1.4)
Requires-Dist: tqdm (==4.51.0)
Requires-Dist: nibabel (>=3.1.0)
Requires-Dist: matplotlib (==3.3.1)
Requires-Dist: pillow (==8.2.0)
Requires-Dist: batchgenerators (>=0.21)
Requires-Dist: pydicom (==2.0.0)
Requires-Dist: SimpleITK (==2.0.2)
Requires-Dist: scikit-image (==0.18.2)

![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/logo_long.png)

[![shield_python](https://img.shields.io/pypi/pyversions/miscnn?style=flat-square)](https://www.python.org/)
[![shield_build](https://img.shields.io/travis/frankkramer-lab/miscnn/master?style=flat-square)](https://travis-ci.org/github/frankkramer-lab/MIScnn)
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The open-source Python library MIScnn is an intuitive API allowing fast setup of medical image segmentation pipelines with state-of-the-art convolutional neural network and deep learning models in just a few lines of code.

**MIScnn provides several core features:**
- 2D/3D medical image segmentation for binary and multi-class problems
- Data I/O, preprocessing and data augmentation for biomedical images
- Patch-wise and full image analysis
- State-of-the-art deep learning model and metric library
- Intuitive and fast model utilization (training, prediction)
- Multiple automatic evaluation techniques (e.g. cross-validation)
- Custom model, data I/O, pre-/postprocessing and metric support
- Based on Keras with Tensorflow as backend

![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/MIScnn.pipeline.png)

## Resources

- MIScnn Documentation: [GitHub wiki - Home](https://github.com/frankkramer-lab/MIScnn/wiki)
- MIScnn Tutorials: [Overview of Tutorials](https://github.com/frankkramer-lab/MIScnn/wiki/Tutorials)
- MIScnn Examples: [Overview of Use Cases and Examples](https://github.com/frankkramer-lab/MIScnn/wiki/Examples)
- MIScnn Development Tracker: [GitHub project - MIScnn Development](https://github.com/frankkramer-lab/MIScnn/projects/1)
- MIScnn on GitHub: [GitHub - frankkramer-lab/MIScnn](https://github.com/frankkramer-lab/MIScnn)
- MIScnn on PyPI: [PyPI - miscnn](https://pypi.org/project/miscnn/)

## Author

Dominik Müller  
Email: dominik.mueller@informatik.uni-augsburg.de  
IT-Infrastructure for Translational Medical Research  
University Augsburg  
Augsburg, Bavaria, Germany

## How to cite / More information

Dominik Müller and Frank Kramer. (2019)  
MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning.  
arXiv e-print: [https://arxiv.org/abs/1910.09308](https://arxiv.org/abs/1910.09308)

```
Article{miscnn,
  title={MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning},
  author={Dominik Müller and Frank Kramer},
  year={2019},
  eprint={1910.09308},
  archivePrefix={arXiv},
  primaryClass={eess.IV}
}
```

Thank you for citing our work.

## License

This project is licensed under the GNU GENERAL PUBLIC LICENSE Version 3.\
See the LICENSE.md file for license rights and limitations.


