Metadata-Version: 2.1
Name: DeepACSA
Version: 0.3.1
Summary: Automatic analysis of transversal muscle ultrasonography images
Project-URL: Homepage, https://github.com/PaulRitsche/DeepACSA
Project-URL: Bug Tracker, https://github.com/PaulRitsche/DeepACSA/issues
Author-email: Paul Ritsche <paul.ritsche@unibas.ch>
License-File: LICENSE
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.9
Requires-Dist: h5py==3.4.0
Requires-Dist: keras==2.9.0
Requires-Dist: matplotlib==3.5.2
Requires-Dist: numpy==1.21.2
Requires-Dist: opencv-contrib-python==4.5.3.56
Requires-Dist: openpyxl==3.0.9
Requires-Dist: pandas==1.3.3
Requires-Dist: pillow==8.3.2
Requires-Dist: scikit-image==0.18.3
Requires-Dist: scikit-learn==0.24.2
Requires-Dist: tensorflow==2.9.0
Requires-Dist: tqdm==4.62.2
Description-Content-Type: text/markdown

# DeepACSA
*Automatic analysis of human lower limb ultrasonography images*

DeepACSA is an open-source tool to evaluate the anatomical cross-sectional area of muscles in ultrasound images using deep learning.
More information about the installtion and usage of DeepACSA can be found in the online [documentation](https://deepacsa.readthedocs.io/en/latest/index.html). You can find information about contributing, issues and bug reports there as well.
If you find this work useful, please remember to cite the corresponding [paper](https://journals.lww.com/acsm-msse/Abstract/9900/DeepACSA__Automatic_Segmentation_of.87.aspx), where more information about the model architecture and performance can be found as well. 

## Quickstart

To quickly start the DeepACSA either open the executable or type 

``python -m Deep_ACSA``

in your prompt once the package was installed and the DeepACSA environment activated.
