Two complementary tools for quantifying retinal vessel density (Tool 1) and FOXO1 protein in endothelial cells (Isolectin B4 positive cells, Tool 2).
| I want to... | Use | Details |
|---|---|---|
| Measure vessel density in ten concentric zones from whole mount retinal images by confocal microscopy | retina-vessel-density CLI |
docs/cli_tool.md |
| Quantify pixel-level FOXO1 signal in isolectin B4-positive vascular regions without writing code | ib4mask (IB4/FOXO1 Portable App) |
docs/portable_app.md |
flowchart LR
A[Retinal image data] --> B{Which question?}
B -->|Concentric-zone vessel density| C[retina-vessel-density CLI]
B -->|IB4 vascular territory + FOXO1| D[ib4mask Portable App]
C --> E[CSV + group-comparison plot]
D --> F[Q1-Q4 CSV/XLSX + QC images]
A small Python package that measures Isolectin B4-positive pixels in ten concentric zones centered on an annotated optic-nerve location, from whole retinal images.
python -m venv .venv
python -m pip install -e ".[test]"
retina-vessel-density analyze --images clean_images --annotations annotations --output results.csvSee docs/cli_tool.md for the full input schema, all commands, the measurement definition, and known limitations.
A retina image-analysis package performs the following.
- Identify retinal vessel area in isolectin B4 images using Gaussian Mixture Model (GMM) segmentation.
- Then, the false discovery rate (FDR) of FOXO1 signal in individual pixels is calculated to distinguish FOXO1 signals from noise.
- FOXO1 intensity in retinal vessel cells is quantified within optic-nerve Q1–Q4 quadrants.
Ships as a self-contained Windows x64 portable build (Python bundled, no install needed) driven entirely from a local browser UI; a source checkout also exposes a CLI and optional High-Performance Computer (HPC)/Slurm-scale nucleus-candidate preparation.
# after extracting the release ZIP next to your TIFF folder
.\launch_browser.cmdSee docs/portable_app.md for download links, setup, the full pipeline, and how to read the output tables.
TBD/
├── README.md # this page
├── docs/
│ ├── cli_tool.md # retina-vessel-density CLI details
│ ├── portable_app.md # ib4mask / IB4/FOXO1 Portable App details
│ └── images/ # screenshots referenced from docs/
├── examples/ # CLI sample images/annotations/results
├── src/retina_vessel_density/ # CLI package source
├── ib4mask/ # ib4mask package source (see ib4mask/README.md)
│ ├── src/ib4mask/ # Flask-based browser UI + analysis pipeline
│ ├── tests/, docs/, examples/, scripts/
│ ├── containers/, hpc/ # optional Slurm/Apptainer nucleus-prep support
│ └── README.md, LICENSE, CITATION.cff
├── tools/ # offline public-release audit scripts (CLI tool)
└── tests/ # CLI package tests
ib4mask is vendored as a self-contained subproject: it keeps its own
license, citation metadata, and test suite (.github/workflows/ib4mask-*.yml
run them independently of the CLI package's CI).
This toolkit does not make network connections, invoke remote shell commands, or read credentials at analysis time. Upload only an audited package directory or its audited archive. Do not upload the surrounding workspace, version-control history from another project, virtual environments, caches, logs, or local provenance records.
Both tools are MIT-licensed. The CLI package's terms are in LICENSE;
ib4mask carries its own identical-terms ib4mask/LICENSE
and ib4mask/CITATION.cff.
Citation metadata are provided in CITATION.cff.
This tool was used in "Cereblon-dependent degradation of FOXO1 by selected next-generation CelMoDs inhibits angiogenesis" (Li, Ohnuki, et al., Laboratory of Cellular Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health), currently under submission; the full citation will be published after acceptance.