CI Segmentation

GPU-enabled instance segmentation for OME-Zarr images and HCS plates, designed for Bilayers and BIOMERO. One CUDA 12.6 environment supports Cellpose 3, Cellpose-SAM v2 and the original Cellpose-SAM, PyTorch StarDist, InstanSeg and Spotiflow, with optional local spot-mask refinement.

What it produces

Input: top-level .ome.zarr stores, including complete HCS plates.
Output: <source>__cisegmentation.ome.zarr, preserving source pixels and adding native label groups for enabled cells, nuclei, cytoplasm, spots, foci or bacteria. The source remains untouched.
DuckDB measurements by default, with SQLite or skip options; spatial measurements, colocalization, tracking and companion geometry databases are opt-in.
Benchmark mode creates a model-comparison gallery instead of normal labels and measurements.

Time and Z are preserved. User-facing channels are one-based; physical parameters use OME-Zarr calibration. Enable at least one segmentation step.

Get started

In BIOMERO, configure the workflow repository at tree/v0.6.4, initialize the image, then select the installed version and input image or plate. The published headless image is:

docker pull cellularimagingcf/w_cisegmentation:v0.6.4

For the Windows local frontend, run create_env.cmd, then launch.cmd. The launcher supports local Python execution and the release-pinned local Docker image. wrapper.py is the container entrypoint; launcher.py is the supported local frontend. See local execution for environment setup, direct examples and the OMERO round trip.

Large images stream bounded tiles. Plate fields use automatically sized worker pools constrained by visible GPU/MIG memory and allocated CPU/RAM. Version 0.6.4 includes MIG-aware sizing and structured resource diagnostics. The original sizing failure was resolved on a 2,844-field H100 MIG run; forced-OOM recovery and scientific comparisons remain separate validation checks. See resource evidence.

Documentation

Topic	Guide
Workflow steps, parameters and output behavior	Workflow
Launcher, direct examples, benchmarks and round trip	Local execution
Streaming, concurrency and timing interpretation	Large inputs
Allocation logs, MIG evidence and recovery checks	Slurm resource checks
Measurement schema, units and query examples	Measurements
Spatial measurements, colocalization, tracking and geometry	Measurement extensions
Retained validation inputs	Public datasets
Checkpoints, offline runtime and Docker caches	Models
Repository structure and test layers	Development
Versions, builds, tags and publication	Releasing

The agent plugin bundles focused skills under skills/ for workflow operation, measurement analysis and geometry analysis. Their references are self-contained for consumers that load skills without this repository's documentation tree.

Optional Gradio and Jupyter builds are maintained separately in bilayers_extra and are excluded from default builds and Docker Hub publication.

Licensed under MIT.