The LazySlide workflow

LazySlide operations are grouped by intent:

Namespace	Purpose	Examples
zs.pp	Prepare spatial regions	find tissue, create tiles, build a graph
zs.seg	Run learned segmentation	tissue, cells, semantic classes, artifacts
zs.tl	Analyze tiles and features	embeddings, predictions, spatial domains
zs.pl	Inspect and visualize results	tissue, tiles, annotations, interactive viewer
zs.io	Exchange annotations	load and export spatial annotations

Dependencies between stages

Most pipelines follow this order:

1. Open a WSI and validate its metadata.

2. Find or import regions of interest.

3. Create one or more tile sets.

4. Optionally score or filter tile quality.

5. Run segmentation, feature extraction, or prediction.

6. Build spatial or slide-level summaries.

7. Visualize and save the result.

Later stages refer to earlier results by key. For example, feature extraction reads a tile_key, then creates a feature table associated with those exact tiles. If tiles are replaced afterward, the old feature rows may no longer correspond to the tile table.

Functions add data in place

Many LazySlide functions mutate wsi and return None. This is intentional:

zs.pp.find_tissues(wsi)                 # adds wsi.shapes["tissues"]
zs.pp.tile_tissues(wsi, 256, mpp=0.5) # adds wsi.shapes["tiles"]
zs.tl.feature_extraction(wsi, "uni")  # adds a feature table

Use key_added to preserve alternative results:

zs.pp.find_tissues(wsi, method="entropy", key_added="tissues_entropy")
zs.pp.tile_tissues(
    wsi,
    256,
    mpp=1.0,
    tissue_key="tissues_entropy",
    key_added="tiles_1mpp",
)

See the output-key reference for the default locations.