Metadata-Version: 2.4
Name: pdnl_sana
Version: 1.0.1
Summary: PDNL Semi-Automatic Neuropathology Analyis (SANA)
Project-URL: Homepage, https://github.com/penndigitalneuropathlab/sana
Author-email: Noah Capp <noahmcapp@gmil.com>
License-File: LICENSE
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.9
Requires-Dist: dill
Requires-Dist: matplotlib
Requires-Dist: numba
Requires-Dist: numpy
Requires-Dist: opencv-python
Requires-Dist: openslide-bin
Requires-Dist: openslide-python
Requires-Dist: pillow
Requires-Dist: scipy
Requires-Dist: shapely
Description-Content-Type: text/markdown

# PDNL Semi Automatic Neuropath Analysis (PDNL-SANA)

## About

PDNL-SANA is a python-based package written by the [Penn Digital Neuropathology Lab](https://www.med.upenn.edu/digitalneuropathologylab/) to formalize our methods of IHC quantification. PDNL-SANA includes functions which facilitate extracting pixel data from a Whole Slide Images (WSI), classifying pixels, and converting positive pixel masks to quantifications. 

## Requirements

python3.9 or greater

## Installation

`python3 -m pip install pdnl_sana`

## Getting Started

We provide several example [Jupyter](https://jupyter.org/) notebooks which contain example code blocks utilizing most of SANA's functionality.

* `docs/source/examples/example0_prepare_images.ipynb` shows how to extract relevant ROI information from a WSI
* `docs/source/examples/example1_process_images.ipynb` provides a sandbox for the preprocessing and pixel classification methods
* `docs/source/examples/example2_normalize_cortex.ipynb` illustrates how to deform a curved section of cortex for more optimal quantification
* `docs/source/examples/example3_quantification.ipynb` has examples of various quantification methods based on the positive pixel masks created by the previous notebooks

For more information, please refer to the [Documentation](https://pdnl-sana.readthedocs.io/en/latest/)

## Roadmap
* GPU Acceleration 
* Automatic GM/WM segmentation
* Generic cell detection/segmentation
* Microglia detection/segmentation
* Structure Tensor Analysis

