Build consensus and stitch across slices

Slice-MSF has two controls that solve different problems. consensus = TRUE combines multiple seeded DCT-subspace runs. stitch_z = TRUE adds graph edges across axial slices. One changes ensemble evidence; the other changes which spatial connections are possible.

Experimental method. Slice-MSF is available for explicit evaluation but is not currently recommended automatically or for production use. Read the Slice-MSF evaluation contract before interpreting this vignette; local regression repairs are complete, but release-level recertification is still pending.

What does stitching change?

The following example uses one run so stitching is the only changed control. Natural segmentation (target_k_global = -1) is used because a global exact-K target is unavailable when slices are independent.

independent <- slice_msf(
  syn$vec, syn$mask, target_k_global = -1,
  r = 6, compactness = 10, min_size = 2, num_runs = 1,
  consensus = FALSE, stitch_z = FALSE, seed = 17
)
stitched <- slice_msf(
  syn$vec, syn$mask, target_k_global = -1,
  r = 6, compactness = 10, min_size = 2, num_runs = 1,
  consensus = FALSE, stitch_z = TRUE, seed = 17
)

Label-invariant continuity across adjacent axial slices.

topology	mean_adjacent_slice_ari
independent slices	0.782
stitched 3-D graph	0.709

Adjacent axial slices from independent-slice and stitched Slice-MSF fits. Each fitted subparcel is colored by its majority known region, giving the same eight-color meaning in all panels.

What to notice. The stitched fit is allowed to propagate parcel identity through axial edges; the independent fit is not. The adjacent-slice ARI is label-invariant and changes from 0.782 to 0.709 on this fixture. Its direction is descriptive of this fixture, not a promise that stitching improves a scientific endpoint.

What does consensus change?

For num_runs > 1, Slice-MSF constructs seeded DCT subspaces and fuses the run partitions. The seed is recorded and the caller’s random-number stream is preserved. Consensus is not a promise of improved accuracy; it is a defined ensemble procedure whose stability and quality must be evaluated for the task.

ensemble <- slice_msf(
  syn$vec, syn$mask, target_k_global = 8,
  r = 6, compactness = 10, min_size = 2, num_runs = 3,
  consensus = TRUE, use_features = TRUE, ensemble_fraction = 0.6,
  stitch_z = TRUE, seed = 17
)

The three seeded runs are nondegenerate and distinct; ARI measures each run’s agreement with the fused eight-parcel result.

run	run_clusters	ari_to_consensus
1	25	0.687
2	26	0.726
3	23	0.682

Agreement of each distinct seeded run with the final consensus partition. Variation across runs is visible rather than hidden behind the fused result.

The exact-K claim above is scoped to this connected mask and requested target; the hidden check makes the vignette fail if it changes. The natural K, requested K, pre/post sizes, repair direction, and effective run controls are available in ensemble$metadata$exact_k_repair. Consensus does not erase run-to-run variation: the table and figure expose it. For parameter tuning and comparison, continue to Choose parameters and Validate and compare clusterings.