Metadata-Version: 2.1
Name: miscnn
Version: 0.1
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
Description: # MIScnn: Medical Image Segmentation with Convolutional Neural Networks
        
        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 and metric support
        - Based on Keras with Tensorflow as backend
        
        ![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/MIScnn.pipeline.png)
        
        ## Getting started: 30 seconds to a MIS pipeline
        
        Create a configuration object to adjust settings as needed.
        
        ```python
        from miscnn.configurations import get_options
        
        # Create configuration object with default settings
        config = get_options()
        # Adjust input parameters
        config["data_path"] = "/home/muellerdo/MRIs_KidneyTumor/"
        config["data_io"] = "nifti"
        # Adjust model parameters
        config["classes"] = 3
        config["dimension"] = "3D"
        config["architecture"] = "unet"
        config["model_variant"] = "standard"
        # Adjust training parameters
        config["epochs"] = 40
        # Adjust prediction parameters
        config["output_path"] = "predictions/"
        ```
        
        Create a neural network model with adjusted configurations.
        
        ```python
        import miscnn.neural_network
        
        model = miscnn.neural_network(config)
        ```
        
        Run a training pipeline including data I/O, preprocessing and data augmentation with default settings.
        
        ```python
        training_set = list(range(0, 100))
        model.train(training_set)
        ```
        
        Run a prediction pipeline and save results under "predictions/".
        
        ```python
        prediction_set = list(range(100, 150))
        model.predict(prediction_set)
        ```
        
        ## Installation
        
        There are two ways to install MIScnn:
        
        - **Install MIScnn from PyPI (recommended):**
        
        Note: These installation steps assume that you are on a Linux or Mac environment. If you are on Windows or in a virtual environment without root, you will need to remove sudo to run the commands below.
        
        ```sh
        sudo pip install miscnn
        ```
        
        - **Alternatively: install MIScnn from the GitHub source:**
        
        First, clone MIScnn using git:
        
        ```sh
        git clone https://github.com/frankkramer-lab/MIScnn.git
        ```
        
        Then, cd to the MIScnn folder and run the install command:
        
        ```sh
        cd MIScnn
        sudo python setup.py install
        ```
        
        ## Author
        
        Dominik Müller\
        Email: dominik.mueller@informatik.uni-augsburg.de\
        IT-Infrastructure for Translational Medical Research\
        University 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.
        
        ## License
        
        This project is licensed under the GNU GENERAL PUBLIC LICENSE Version 3.\
        See the LICENSE.md file for license rights and limitations.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
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.
Description-Content-Type: text/markdown
