---

name: tracking-segmentation description: "Plan and debug Dipy tractography, streamline clustering, bundle segmentation, brain masking, tissue classification, and tractometry handoffs." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Dipy Tracking And Segmentation

Use this sub-skill when a task mentions tractography, streamlines, seeds, direction getters, stopping criteria, QuickBundles, RecoBundles, LabelsBundles, median Otsu masking, tissue classification, AFQ, BUAN, or tractometry. Keep this file as the router; detailed APIs, CLI commands, workflow recipes, and troubleshooting live in bundled references.

Route First

• Need peaks, ODFs, SH coefficients, PAM creation, or diffusion model fitting before tracking: use ../reconstruction-models/, then return here for tractography.
• Need tractogram load/save, StatefulTractogram, file formats, bvals/bvecs, reference image, space, origin, or bbox checks: use ../io-data/.
• Need affine/SyN registration, SLR design, bundlewarp, or transform estimation: use ../registration-alignment/.
• Need generic dipy_* entry-point discovery, workflow parser behavior, or help probing: use ../cli-workflows/.

Bundled Runtime References

• references/api-reference.md lists the tracking, segmentation, masking, tissue-classification, AFQ, BUAN, and owned CLI surfaces.
• references/workflows.md gives safe planning recipes for local tracking, PFT, streamline manipulation, QuickBundles, RecoBundles/LabelsBundles, masking, tissue classification, AFQ, and BUAN handoffs.
• references/troubleshooting.md maps common tracking/segmentation symptoms to likely causes, concrete recovery actions, validation checks, and routing boundaries.
• scripts/dipy_streamline_smoke.py runs deterministic synthetic Streamlines, QuickBundles, seed, and stopping-criterion checks without downloads or file writes.

Safe Defaults

• Start with tiny synthetic arrays or a small already-local tractogram before whole-brain tracking, bundle recognition, or group BUAN analysis.
• Treat coordinates explicitly: seeds and streamline points live in the point space implied by the affine, while masks and stopping maps are voxel grids sampled through that affine.
• Prefer deterministic seed masks, fixed random seeds, bounded minlen/maxlen, and numeric checks before optional visualization.
• Do not assume FURY, matplotlib, PyTorch, TensorFlow, or neural-network model weights are available; use classic numeric and masking fallbacks when optional surfaces are absent.

Minimal Validation

Run the bundled smoke script before deeper debugging:

python scripts/dipy_streamline_smoke.py

Expected output is JSON with ok: true, four synthetic streamlines, two clusters, nonzero seed count, zero empty-mask seeds, and positive streamline lengths.