Measurements (fast, whole-volume regionprops)

skimage.measure.regionprops needs the full labelled + intensity array in RAM — fine for one tile, not for a hundred-thousand-object OME-ZARR.

Interactively, in napari

napari-chunked-regionprops is built for this — its "Measure" dock widget computes area/centroid/intensity stats directly off a Labels layer's dask/zarr-backed array, out-of-core, and scales with chunk count rather than object count. It's the best fit for measuring every object in a store this size, not just a cropped region — see View image + labels in napari. Bundled in patchworks[napari].

For interactively inspecting individual cells by clicking in the viewer (not all objects at once), the napari-skimage-regionprops plugin's table widget also works well — point it at a cropped region rather than the full volume, since it loads its input fully into memory.

Headless / scripted

Use dask-image's ndmeasure, which computes directly on the dask/zarr-backed arrays, chunk-parallel, without materializing the volume:

pip install dask-image

import dask.array as da
from dask_image.ndmeasure import area, center_of_mass, mean, standard_deviation

labels = da.from_zarr("results/image.zarr", component="labels/cyto_labels/0")
image = da.from_zarr("results/image.zarr", component="0")[0]  # channel 0, level 0

ids = da.unique(labels[labels > 0]).compute()
areas = area(image, labels, ids).compute()               # voxel counts
means = mean(image, labels, ids).compute()                # mean intensity
stds = standard_deviation(image, labels, ids).compute()
centroids = center_of_mass(image, labels, ids).compute()  # voxel coords (z, y, x)

Multiply areas by the voxel's physical volume and centroids by the pixel size (both read straight from the OME-ZARR's own multiscales metadata) to get µm-scale measurements.