senoquant 1.0.0b1

Napari plugin for spatial quantification of senescence markers in tissue imaging

SenoQuant

SenoQuant is a versatile Napari plugin designed for comprehensive, accurate, and unbiased spatial quantification and prediction of senescence markers across diverse tissue contexts.

Features

• Multi-Model Segmentation: Nuclear and cytoplasmic segmentation with 5 built-in models
  • StarDist ONNX (2D/3D)
  • Cellpose SAM
  • Morphological operations (dilation, perinuclear rings)
• Spot Detection: Detect and quantify punctate senescence markers
  • Undecimated B3-spline wavelet (UDWT)
  • Rotational morphological processing (RMP)
• Quantification: Extract intensity, morphology, and spot metrics
  • Per-cell marker intensities
  • Morphological descriptors
  • Spot counting and colocalization
• Batch Processing: Automated analysis of entire image folders
  • Profile save/load for reproducibility
  • Multi-scene file support
• File Format Support: Microscopy formats via BioIO
  • OME-TIFF, ND2, LIF, CZI, Zarr, and more

Installation

Prerequisites

SenoQuant requires Python 3.11+ and napari:

conda create -n senoquant python=3.11
conda activate senoquant
pip install "napari[all]"

Or using uv (faster installer):

conda create -n senoquant python=3.11
conda activate senoquant
pip install uv
uv pip install "napari[all]"

Install SenoQuant

pip install senoquant

Or with uv:

uv pip install senoquant

Model files are downloaded automatically on first use from Hugging Face. To override the model repository, set SENOQUANT_MODEL_REPO environment variable.

For GPU acceleration (Windows/Linux with CUDA):

pip install senoquant[gpu]

Note: The first launch of napari and the SenoQuant plugin will be slower as napari initializes and SenoQuant downloads model files (~1.3 GB) from Hugging Face. Subsequent launches will be faster as models are cached locally.

Quick Start

1. Launch napari and open your image:

   napari
   

   File → Open File(s)... → Select your image

2. Open SenoQuant plugin:
   Plugins → SenoQuant

3. Run segmentation:
   Segmentation tab → Select nuclear channel → Choose model → Run

4. Detect spots (optional):
   Spots tab → Select channel → Choose detector → Run

5. Export quantification:
   Quantification tab → Configure features → Export

6. Batch process (optional):
   Batch tab → Configure settings → Run Batch

Documentation

Full documentation is available at https://haamsree.github.io/senoquant-dev/

• Installation Guide
• Quick Start Tutorial
• Segmentation Models
• Spot Detection
• Quantification Features
• Batch Processing
• API Reference

Development

See the Contributing Guide for development setup instructions.