Description Recommended roadmap
Add a collection API above OMEArrow.
Introduce OMEArrowCollection (or similar) for a table of related assets per sample/FOV (image, profiles, labels, etc.).
Keep current OMEArrow unchanged as the single-record primitive.
Make dtype and annotation support first-class.
Replace pa.list_(pa.uint16()) planes/chunks with a dtype-flexible representation.
Implement real masks/labels schema (instead of null) and typed spatial geometries/regions.
This is critical for profiles/segmentation and spatial ecosystem compatibility.
Build adapters instead of forcing external ecosystems into core.
Add ome_arrow.adapters.muon: from_mudata, to_mudata.
Add ome_arrow.adapters.spatial: from_spatialdata, to_spatialdata.
Keep these as optional dependencies (muon, anndata, spatialdata) so base install stays light.
Focus performance work on Arrow-native execution paths.
Minimize as_py() conversions.
Add chunk-aware ROI slicing that works without requiring dense planes.
Support direct ROI from Zarr chunks for lazy reads instead of per-plane decode loops.
Strengthen benchmarking around real domain cases.
Keep the current lazy canary benchmark.
Add benchmarks for multi-array Zarr group ingest, ROI over chunked stores, and adapter round-trips.
Use these to gate regressions before wider adoption.
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Recommended roadmap
Add a collection API above
OMEArrow.OMEArrowCollection(or similar) for a table of related assets per sample/FOV (image,profiles,labels, etc.).OMEArrowunchanged as the single-record primitive.Make dtype and annotation support first-class.
pa.list_(pa.uint16())planes/chunks with a dtype-flexible representation.null) and typed spatial geometries/regions.Build adapters instead of forcing external ecosystems into core.
ome_arrow.adapters.muon:from_mudata,to_mudata.ome_arrow.adapters.spatial:from_spatialdata,to_spatialdata.muon,anndata,spatialdata) so base install stays light.Focus performance work on Arrow-native execution paths.
as_py()conversions.planes.Strengthen benchmarking around real domain cases.