PDNL Semi Automatic Neuropath Analysis (PDNL-SANA)

About

PDNL-SANA is a python-based package written by the Penn Digital Neuropathology Lab 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 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

Roadmap

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

Development Status
- 4 - Beta
Intended Audience
- Science/Research
License
- OSI Approved :: MIT License
Operating System
- OS Independent
Programming Language
- Python :: 3
Topic
- Scientific/Engineering :: Medical Science Apps.

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