pyspatialhistologyanalysis
Package using StarDist and Python that performs object detection and spatial analysis on H&E images
https://github.com/ajinkya-kulkarni/pyspatialhistologyanalysis
Science Score: 49.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (9.7%) to scientific vocabulary
Keywords
Repository
Package using StarDist and Python that performs object detection and spatial analysis on H&E images
Basic Info
- Host: GitHub
- Owner: ajinkya-kulkarni
- License: gpl-3.0
- Language: Python
- Default Branch: main
- Homepage: https://pyspatialhistologyinformation.streamlit.app/
- Size: 209 MB
Statistics
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 3
Topics
Metadata Files
README.md
Demonstrating PySpatialHistologyAnalysis
App Overview
This is primarily a web application for analyzing H&E stained images using the PySpatialHistologyAnalysis package, which utilizes the StarDist packages under it's hood. The application allows the user to upload an H&E image. It first stain normalizes the image and then performs object (nuclei) detection on the image using the StarDist2D model. The detected objects are then highlighted and displayed alongside the original image. Spatial analysis is then performed on the detected nuclei, and finally a spreadsheet of all the results is displayed.
Dependencies
The app is built using the Streamlit framework and requires the dependencies as mentioned in the requirements.txt file.
Using the app
To use the app, simply upload an H&E image using the file upload widget and click the "Analyze" button. The app will then perform object detection on the image using the StarDist2D model and display the results alongside the original image. If an error occurs during image analysis, an error message will be displayed. Note that the app works best for images smaller than 1000x1000 pixels.
References:
Owner
- Name: Ajinkya Kulkarni
- Login: ajinkya-kulkarni
- Kind: user
- Location: Göttingen
- Company: Max Planck Institute for Multidisciplinary Sciences
- Website: https://orcid.org/0000-0003-1423-3676
- Twitter: kulkajinkya
- Repositories: 5
- Profile: https://github.com/ajinkya-kulkarni
Image Data Scientist @mpi_nat working in Translational Oncology
GitHub Events
Total
- Watch event: 1
- Push event: 4
Last Year
- Watch event: 1
- Push event: 4
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Ajinkya Kulkarni | k****a@g****m | 210 |
Issues and Pull Requests
Last synced: about 2 years ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- Pillow *
- matplotlib *
- networkx *
- numpy *
- pandas *
- scikit-image *
- scikit-learn *
- scipy *
- stardist *
- streamlit *
- tensorflow-cpu *