---

name: experimental-imaging description: "Use Squidpy experimental SpatialData image APIs for tissue masks, tiling, image QC, stain normalization, feature extraction, and tiling-artifact QC/stitching." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Squidpy Experimental Imaging

Use this sub-skill when a task needs squidpy.experimental image workflows on SpatialData: tissue detection, tile generation, tile-based image QC, experimental per-cell image features, stain normalization/decomposition, or tiling-artifact scoring and stitch-group annotation.

Use This For

• Detecting tissue in sdata.images[...] and writing masks to sdata.labels[...] with squidpy.experimental.im.detect_tissue.
• Creating regular or Visium-spot-centered tile polygons in sdata.shapes[...] with make_tiles or make_tiles_from_spots.
• Computing tile-level image QC into sdata.tables[...] and sdata.shapes[...] with experimental.im.qc_image, then plotting with experimental.pl.qc_image.
• Extracting per-cell features from SpatialData images, labels, shapes, and tables with experimental.im.calculate_image_features.
• Fitting, applying, and validating H&E stain normalization/decomposition with StainReference, ReinhardParams, MacenkoParams, and VahadaneParams.
• Scoring segmentation tile-boundary artifacts with experimental.tl.calculate_tiling_qc, assigning stitch groups with assign_stitch_groups, and plotting with experimental.pl.tiling_qc.

Route Elsewhere

• Use image-analysis for stable squidpy.im.ImageContainer, stable sq.im.process, stable segmentation, and stable image feature extraction.
• Use datasets-and-io for dataset downloads, squidpy.datasets.*, squidpy.read.*, and SpatialData sample acquisition.
• Use visualization for general squidpy.pl spatial plots and graph/statistics plotting.
• Use graph-analysis for spatial neighbor graphs and image-independent spatial statistics.

Start Here

1. Read references/experimental-workflows.md for safe workflow order, SpatialData key contracts, and storage locations.
2. Read references/api-reference.md for signatures, defaults, parameter dataclasses, outputs, and plotting entry points.
3. Read references/troubleshooting.md when experimental API changes, scale/key ambiguity, masks, stain validation, Weka, tiling thresholds, or memory/parallelism fail.
4. Run scripts/experimental_imaging_smoke.py --help or the default smoke command to check imports, dataclass validation, signatures, and a tiny generated-data stain/reference path without downloads.

Safe Minimal Pattern

import squidpy as sq

sq.experimental.im.detect_tissue(
    sdata,
    image_key="image",
    scale="auto",
    new_labels_key="image_tissue",
    inplace=True,
)
sq.experimental.im.make_tiles(
    sdata,
    image_key="image",
    tissue_mask_key="image_tissue",
    tile_size=(224, 224),
    min_tissue_fraction=0.5,
    preview=False,
)

Treat squidpy.experimental APIs as volatile. Prefer explicit image_key, labels_key, shapes_key, table_key, scale, and preview=False in automation, and inspect sdata.images, sdata.labels, sdata.shapes, and sdata.tables after every in-place step.