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.ipynbshows how to extract relevant ROI information from a WSIdocs/source/examples/example1_process_images.ipynbprovides a sandbox for the preprocessing and pixel classification methodsdocs/source/examples/example2_normalize_cortex.ipynbillustrates how to deform a curved section of cortex for more optimal quantificationdocs/source/examples/example3_quantification.ipynbhas 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
1 maintainer
Metadata
1 maintainer
Release files for pdnl-sana 1.0.17
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Source distribution (sdist)
| File | Size | Uploaded | |
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| pdnl_sana-1.0.17.tar.gz | 66.6 MB | Details |
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|---|---|---|---|---|
| pdnl_sana-1.0.17-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 66.7 MB
Release files / pdnl_sana-1.0.17.tar.gz
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