How LazySlide stores results

LazySlide uses WSIData as the analysis container. The original WSI remains the pixel source; analysis results are organized into spatial shapes, tables, images, and metadata.

The important collections

Collection	Typical content
wsi.shapes	tissue polygons, tiles, cells, annotations
wsi.tables	feature embeddings and other annotated matrices
wsi.images	raster outputs such as generated or segmentation images
wsi.attrs	specifications and metadata describing results

Inspect the object before guessing a key:

print(wsi.shapes.keys())
print(wsi.tables.keys())
print(wsi.images.keys())
print(wsi.attrs.keys())

The shorthand wsi["key"] retrieves a named element, but explicit collections are clearer while debugging.

Spatial coordinates

Shape geometries are expressed in the level-0 image coordinate system. LazySlide records how a tile set should be read at its requested MPP; the polygons still align with the full-resolution slide.

Associations matter

A feature table is associated with the shape table that supplied its image regions. For default tiles, a model named uni normally creates uni_tiles. For a custom tile set:

zs.pp.tile_tissues(wsi, 256, mpp=1.0, key_added="coarse_tiles")
zs.tl.feature_extraction(wsi, "uni", tile_key="coarse_tiles")
# Default feature key: uni_coarse_tiles

Pass tile_key, feature_key, and key_added explicitly in reusable pipelines. This makes dependencies visible and avoids accidentally reading a similarly named result.

Persistence

Open a source WSI with a backing store when results must survive the Python session:

wsi = zs.open_wsi("slide.svs", backed_file="slide-analysis.zarr")
# ...add results...
wsi.write(overwrite=True)

The backing store does not replace the source slide. Keep the source path valid when reopening an analysis that still needs to fetch image pixels.