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Roadmap

Next-generation tools under active development in the WayScience organization, forming the foundation of the Cytomining roadmap.

What gaps does our roadmap address?

The current Cytomining stack was designed around 2D single-cell data from CellProfiler. As the field moves toward 3D organoid imaging, single-cell profiling, larger-scale screens, and deep learning feature extraction, several gaps have emerged: no standardized image catalog, images and features stored separately, limited 3D support, and hit calling that collapses single-cell heterogeneity. We are building the tools below to close each of these gaps and raise the computational pipeline to support the demands of data collection and goals of the moment.

Experimental tools

These tools are under active development in the WayScience organization. For production-ready tools, see the Tools page.

buscar icon buscar

GitHub stars for WayScience/buscar
Hit calling — identifies biologically active perturbations from single-cell morphology profiles using distribution-level scoring.
Problem: Population-level hit calling averages away cell-to-cell variation, hiding heterogeneous responses and rare subpopulations.

iceberg-bioimage icon iceberg-bioimage

GitHub stars for WayScience/iceberg-bioimage
Data cataloging — scans bioimaging stores and publishes image metadata to Cytomining-compatible Parquet warehouses via Apache Iceberg.
Problem: Raw bioimaging archives have no standard catalog — finding, versioning, and joining images to downstream data requires bespoke scripts per lab.

OME-arrow icon OME-arrow

GitHub stars for WayScience/OME-arrow
Image storage — stores microscopy images alongside metadata and derived data in a unified, queryable Apache Arrow format.
Problem: Images and feature tables live in separate systems — linking a numeric outlier back to its source cell requires error-prone manual joins across formats.

ZEDprofiler icon ZEDprofiler

GitHub stars for WayScience/zedprofiler
3D feature extraction — extracts morphological features from volumetric microscopy images for CPU-efficient high-content profiling.
Problem: Classical profiling tools only extract 2D features, leaving organoid and z-stack experiments without a CPU-efficient extractor.

Roadmap pipelines

Standard (2D)

🔬 Raw Images
→→
OME-arrow store
→
📊 Feature Extraction
extract
→
CytoTable harmonize
→→
Pycytominer process
→
buscar hit call

3D Organoid

🔬 Raw Images
→→
OME-arrow store
→
ZEDprofiler 3D extract
→
CytoTable harmonize
→→
Pycytominer process
→
buscar hit call

Yellow = new 3D-capable step. Purple = new data infrastructure. Blue = existing Cytomining tools.