ikeda042

ikeda042 / PhenoPixel

Public

An OpenCV based image analysis web application (Deployed at Hiroshima University)

bacteria bioinformatics-pipeline cell-analysis cell-contour fastapi
195
0
85% credibility
Found May 28, 2026 at 195 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

PhenoPixel is a research tool that helps scientists analyze microscopy images of cells. It takes raw ND2 microscopy files, automatically detects and extracts individual cells, lets researchers label and filter them, then runs batch analytics to measure cell sizes, shapes, and fluorescence patterns across entire cell populations. The results can be exported as plots, heatmaps, and CSV data for use in research papers.

How It Works

1
🔬 You upload your microscopy files

You start by uploading your ND2 microscopy files containing cell images into the system.

2
✨ The system finds and outlines your cells

PhenoPixel automatically detects cell boundaries and extracts individual cells from your images, creating a clean database of cell shapes.

3
You choose how to review your cells
🤖
Auto-filter (recommended)

The system uses shape analysis to automatically identify real cells and filter out debris.

✋
Manual review

You click through cells one by one to mark which ones are real cells.

4
📊 You run batch analysis on your cell population

Once your cells are labeled, you can run bulk analytics to measure cell lengths, areas, and fluorescence patterns across thousands of cells at once.

5
🗺️ You visualize patterns across your cells

The system generates heatmaps and plots showing how protein signals are distributed along each cell, helping you spot patterns.

6
📦 You export your results

You download your analyzed data as CSV files or image plots, ready to use in your research paper or presentation.

🎉 You have quantitative single-cell data

From raw microscopy images, you now have clean measurements of thousands of individual cells, with patterns and statistics ready for your research.

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Star Growth

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AI-Generated Review

What is PhenoPixel?

PhenoPixel is a web-based microscopy analysis platform that transforms raw ND2 image files into quantified single-cell data. The backend, built with Python and FastAPI, handles image processing through OpenCV while the React frontend provides an interactive interface for running workflows. Users upload microscopy files, extract cell contours automatically, annotate individual cells, and run batch analytics across entire populations. The system outputs measurements like cell length, area, and fluorescence intensity, along with visualizations like heatmaps and contour overlays.

Why is it gaining traction?

The project fills a specific gap for biologists who need to process ND2 files without writing custom scripts. Its auto-annotation feature attempts to separate real cells from debris using geometric heuristics, reducing manual cleanup time. The bulk analytics engine supports multiple analysis modes including heatmap generation, aggregation ratios, and contour alignment. Docker deployment with Traefik integration makes it deployable in research environments where IT support is limited.

Who should use this?

Cell biologists and microscopy researchers working with phase-contrast and fluorescence images who need to extract quantitative data from time-lapse experiments. Lab managers running standardized assays who want reproducible, shareable analysis pipelines. Computational biology teams needing a web interface for non-technical collaborators to review and annotate cell populations.

Verdict

PhenoPixel delivers a functional end-to-end pipeline for single-cell analysis from microscopy data, though the 195-star count and 0.85% credibility score indicate a niche tool maintained by a single author at an academic institution. Documentation is thorough for the core workflows, but test coverage and community support are limited. If your lab works with ND2 files and needs a turnkey solution for cell extraction and batch analytics, this is worth evaluating. For teams needing broader ecosystem support, consider whether the academic maintenance model aligns with your long-term needs.

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