Metadata-Version: 2.1
Name: platipy
Version: 0.4.1
Summary: Processing Library and Analysis Toolkit for Medical Imaging in Python
Home-page: https://pyplati.github.io/platipy/
Author: Phillip Chlap & Robert Finnegan
Author-email: phillip.chlap@unsw.edu.au
License: Apache 2.0 License
Description: # PlatiPy 
        ## Processing Library and Analysis Toolkit for Medical Imaging in Python
        
        PlatiPy is a library of **amazing** tools for image processing and analysis - designed specifically
        for medical imaging! 
        
        Check out the [PlatiPy documentation](https://pyplati.github.io/platipy/) for more info.
        
        This project was motivated by the need for a simple way to use, visualise, process, and analyse 
        medical images. Many of the tools and algorithms are designed in the context of radiation therapy,
        although they are more widely applicable to other fields that use 2D, 3D, or 4D imaging.
        
        PlatiPy is written in Python, and uses SimpleITK, VTK, and standard Python libraries. Jupyter
        notebooks are provided where possible, mainly for guidance on getting started with using the tools.
        We welcome feedback and contributions from the community (yes, you!) and you can find more
        information about contributing [here](https://pyplati.github.io/platipy/contributing.html).
        
        ## What can I do with **platipy**?
        A lot! A good place to start is by looking in the 
        [examples directory](https://github.com/pyplati/platipy/tree/master/examples).
        
        Some examples of what PlatiPy can do:
         - DICOM organising and converting:
            * Bulk convert from multiple series and studies with a single function
            * Convert DICOM-RT structure and dose filesto NIfTI images
            * Create DICOM-RT structure files from binary masks e.g. from automatic contouring algorithms
         - Image registration
            * Register images and transform labels with a few lines of code
            * Linear transformations: rigid, affine, similarity
            * Non-linear deformable transformations: demons, b-splines
            * Multiple metrics for optimisation
         - Atlas-based segmentation
            * A suite of tools that can be used out-of-the-box
            * Includes advanced algorithms for 
              [iterative atlas selection](https://doi.org/10.1088/1361-6560/ab652a/) and 
              [vessel splining](https://doi.org/10.1088/1361-6560/abcb1d/)
         - Synthetic deformation field generation
            * Simulate anatomically realistic shifts, expansions, and bending
            * Compare DIR results from clinical systems
         - Basic tools for image processing and analysis
            * Computing label similarity metrics: DSC, mean distance to agreement, Hausdorff distance, and more
            * Cropping images to a region of interest
            * Rotate images and generate maximum/mean intensity projections (beams eye view modelling)
        
        A major part of this package is **visualisation**, and some examples are shown below!
        
        #### Visualise some contours
        
        ``` python
        from platipy.imaging import ImageVisualiser
        
        vis = ImageVisualiser(image)
        vis.add_contour(contours)
        fig = vis.show()
        ```
        
        ![Figure 1](assets/figure_1.png)
        
        #### Register some images
        
        ```python
        from platipy.imaging.registration.linear import linear_registration
        
        image_2_registered, tfm = linear_registration(
        image_1,
        image_2
        )
        
        vis = ImageVisualiser(image_1)
        vis.add_comparison_overlay(image_2_registered)
        fig = vis.show()
        ```
        
        ![Figure 2](assets/figure_2.png)
        
        #### Calculate deformation vector fields
            
        ```python
        from platipy.imaging.registration.deformable import fast_symmetric_forces_demons_registration
        
        image_2_deformed, tfm_dir, dvf = fast_symmetric_forces_demons_registration(
        image_1,
        image_2_registered
        )
        
        vis = ImageVisualiser(image_2_deformed, axis="z")
        vis.add_vector_overlay(
            dvf,
            subsample=12,
            arrow_scale=1,
            arrow_width=2,
            colormap=plt.cm.magma,
            name="DVF magnitude [mm]",
            color_function="magnitude"
        )
        fig = vis.show()
        ```
        
        ![Figure 3](assets/figure_3.png)
        
        ## Getting started
        There aren't many requirements, just an installed Python interpreter (3.6 or greater). The list of
        required Python packages can be found in requirements.txt.
        
        PlatiPy can be installed with **pip**:
        
        ```bash
        pip install platipy
        ```
        
        ## Authors
        
        * **Phillip Chlap** - [phillip.chlap@unsw.edu.au](phillip.chlap@unsw.edu.au)
        * **Robert Finnegan** - [robert.finnegan@sydney.edu.au](robert.finnegan@sydney.edu.au)
        
Keywords: medical imaging,visualisation,registration,radiotherapy,image analysis
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Development Status :: 4 - Beta
Description-Content-Type: text/markdown
