Metadata-Version: 2.1
Name: miscnn
Version: 0.34
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, 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)
        
        ## Getting started: 30 seconds to a MIS pipeline
        
        Create a Data I/O instance with an already provided interface for your specific data
        format.
        
        ```python
        from miscnn.data_loading.data_io import Data_IO
        from miscnn.data_loading.interfaces.nifti_io import NIFTI_interface
        
        # Create an interface for kidney tumor CT scans in NIfTI format
        interface = NIFTI_interface(pattern="case_0000[0-2]", channels=1, classes=3)
        # Initialize data path and create the Data I/O instance
        data_path = "/home/mudomini/projects/KITS_challenge2019/kits19/data.original/"
        data_io = Data_IO(interface, data_path)
        ```
        
        Create a Preprocessor instance to configure how to preprocess the data into batches.
        
        ```python
        from miscnn.processing.preprocessor import Preprocessor
        
        pp = Preprocessor(data_io, batch_size=4, analysis="patchwise-crop", patch_shape=(128,128,128))
        ```
        
        Create a deep learning neural network model with a standard U-Net architecture.
        
        ```python
        from miscnn.neural_network.model import Neural_Network
        from miscnn.neural_network.architecture.unet.standard import Architecture
        
        unet_standard = Architecture()
        model = Neural_Network(preprocessor=pp, architecture=unet_standard)
        ```
        Congratulations to your ready-to-use Medical Image Segmentation pipeline including data I/O, preprocessing and data augmentation with default setting.
        
        Let's run a model training on our data set. Afterwards, predict the segmentation of a sample using the fitted model.
        
        ```python
        # Training the model with all except one sample for 500 epochs
        sample_list = data_io.get_indiceslist()
        model.train(sample_list[0:-1], epochs=500)
        
        # Predict the one remaining sample
        pred = model.predict([sample_list[-1]], direct_output=True)
        ```
        
        Now, let's run a 5-fold Cross-Validation with our model, create automatically evaluation figures and save the results into the directory "evaluation_results".
        
        ```python
        from miscnn.evaluation.cross_validation import cross_validation
        
        cross_validation(sample_list, model, k_fold=5, epochs=100,
                         evaluation_path="evaluation_results", draw_figures=True)
        ```
        
        ## 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
