miscnn 1.4.0

Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

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

Resources

• MIScnn Documentation: GitHub wiki - Home
• MIScnn Tutorials: Overview of Tutorials
• MIScnn Examples: Overview of Use Cases and Examples
• MIScnn Development Tracker: GitHub project - MIScnn Development
• MIScnn on GitHub: GitHub - frankkramer-lab/MIScnn
• MIScnn on PyPI: PyPI - miscnn

Author

Dominik Müller
Email: [e-mail]
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

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.