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name: "3d-workflows" description: "Route StarDist 3D users through Config3D, ray and anisotropy selection, volume data preparation, training, local or pretrained inference, thresholds, and bounded large-volume prediction." metadata: disco-role: operating disable-model-invocation: true license: BSD 3-Clause

StarDist 3D workflows

Use this sub-skill for volumetric images, Config3D, StarDist3D, ray factories, anisotropic microscopy, and 3D block inference. The required baseline is CPU TensorFlow 2.x, StarDist, and its compiled CPU extensions. CUDA TensorFlow and OpenCL/gputools are optional accelerators; a visible CUDA device does not prove OpenCL availability. BioImage.IO, QuPath, and OBJ/export are optional integrations, not part of the CPU baseline; their workflows belong to deployment-integration.

Route

1. Establish ZYX/ZYXC axes, channel count, label convention, physical anisotropy, and a memory budget.
2. Choose one ray object and make its length equal Config3D.n_rays and every distance tensor's final dimension.
3. Validate paired image/mask volumes, grid-compatible patches, normalization, and class mappings before training.
4. Load/build a local or pretrained model, then use the verified StarDist3D.predict_instances contract with deliberate sparse/dense, threshold, scale, and tiling choices.
5. Use predict_instances_big only after checking per-axis object-size and the strict constraint min_overlap+2*context < block_size; consult troubleshooting.md for recovery.

Detailed contracts and recipes:

• api-reference.md: Config3D, data, model, prediction, training, and threshold APIs.
• workflows.md: 3D preparation, training, pretrained/local inference, multiclass, normalization, and smoke routes.
• rays-and-anisotropy.md: ray factories, physical conventions, scaling, and dist_loss_weights.
• large-data.md: n_tiles, block inference, labels_out, memory, and seam constraints.
• troubleshooting.md: install/import, backend, axes/rays/grid, model-path, threshold, tiling, and OOM recovery.

Native repository evidence for this sub-skill is recorded by relative path: stardist/models/model3d.py, stardist/models/base.py, stardist/rays3d.py, stardist/big.py, stardist/geometry/geom3d.py, examples/3D/1_data.ipynb, examples/3D/2_training.ipynb, examples/3D/3_prediction.ipynb, examples/other3D/README.md, tests/test_model3D.py, tests/test_stardist3D.py, tests/test_nms3D.py, and tests/test_big.py. These paths are provenance only; runtime use requires no source checkout.