pdnl-sana 1.0.0

PDNL Semi-Automatic Neuropathology Analyis (SANA)

Semi Automatic Neuropath Analysis (SANA)

About

SANA is a python-based package that was written by the Penn Digital Neuropathology Lab to formalize our methods of IHC quantification. SANA includes functions which facilitate extracting pixel data from a Whole Slide Images (WSI), classifying pixels, and converting positive pixel masks to quantifications.

Installation

Python

• Install >Python 3.9

Dependencies

pip

python3 -m pip install -r requirements.txt

OpenSlide (may be required)

If the OpenSlide binaries are not found when running import openslide, following these instructions

Getting Started

We provide several example Jupyter notebooks which contain example code blocks utilizing most of SANA's functionality.

• examples/example0_prepare_images.ipynb shows how to extract relevant ROI information from a WSI
• examples/example1_process_images.ipynb provides a sandbox for the preprocessing and pixel classification methods
• examples/example2_normalize_cortex.ipynb illustrates how to deform a curved section of cortex for more optimal quantification
• 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

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