Metadata-Version: 1.2
Name: autosegl3
Version: 0.4.0
Summary: Tool for automatically segmenting muscle and fat tissue in CT images acquired at the 3rd vertebral level
Home-page: https://github.com/rbrecheisen/autosegl3
Author: Ralph Brecheisen
Author-email: ralph.brecheisen@gmail.com
License: UNKNOWN
Description: =========================
        L3 Auto-segmentation Tool
        =========================
        
        
        
        
        
        
        Tool for automatically segmenting muscle and fat tissue in CT images acquired at the 3rd vertebral level
        
        The AutoSegL3 tool allows a data manager to train a deep learning model that automatically segments
        muscle and fat tissue in CT images taken at the 3rd vertebral (L3) level. To train the deep learning model
        the tool needs a collection of L3 images and corresponding TAG files that contain the labels of each tissue
        to be segmented. To run the trained model on previously unseen CT images the tool only needs a collection of
        L3 images. The tool will then produce a mask for each L3 image that outlines the location of the muscle and
        fat regions.
        
        For training, if default parameters are used, all the data manager has to do is point the tool to a directory
        containing L3 images and corresponding TAG files. From this directory, an HDF5 file will be generated. During
        this process the images and TAG files will be checked for certain characteristics like a common dimension of
        512 by 512 pixels, pixels containing normalized Hounsfield units, etc. Any images that do pass this initial
        quality check will be reported in a text file.
        
        For testing the training procedure, the tool also has to be pointed to a directory containing both L3 images
        and TAG files. However, only the L3 images will be used for generating segmentations. The TAG files will be used
        to evaluate the quality of the segmentations. This step will also produce a summary report containing some
        performance metrics, e.g., Dice scores. Note that the testing phase is only meant to obtain realistic performance
        metrics. To use the model for prediction, train it on all data you have (see next section).
        
        For model preparation, train it on all data you have. Generate a CSV database containing certain clinical scores
        for each L3 image, e.g., SMRA, muscle index, SAT index and VAT index (what other scores can we think of?). This
        database can then be used to visualize the spread of scores across all images in the training data. When a new
        image is predicted you can also highlight its position within the spread of the training scores.
        
        For prediction, the tool has to be pointed to a directory containing only L3 images.
        
        
        
        
        Features
        --------
        
        * TODO
        
        Credits
        -------
        
        This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
        
        .. _Cookiecutter: https://github.com/audreyr/cookiecutter
        .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
        
        
        =======
        History
        =======
        
        0.1.0 (2021-02-04)
        ------------------
        
        * First release on PyPI.
        
Keywords: autosegl3
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
