Version Compatibility

Reference examples tested with: napari 0.4.18+, napari-imc 0.7+, numpy 1.26+, scikit-learn 1.4+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Notes specific to this skill: core napari cannot open .mcd -- the napari-imc plugin reads raw Fluidigm/Standard BioTools files in machine coordinates. napari add_labels edits integer masks (segmentation QC); add_points with a features table holds per-cell categorical labels. Mantis Viewer is a standalone Electron app from CANDELbio/Parker Institute (NOT the Bodenmiller group). napari has no journal paper -- cite the Zenodo DOI.

Interactive Annotation

"Manually annotate cell types in my IMC data" -> Look at the image with masks overlaid to label cells, generate ground truth, and catch the artifacts a cell table hides.

• Python: napari.Viewer with napari-imc (raw .mcd), add_labels/add_points
• R: cytomapper::cytomapperShiny (gate then see the gate on tissue)

The Single Most Important Modern Insight -- annotation is the pixels-to-cell-table bridge, and the image vetoes the table

The IMC pipeline collapses a multi-GB pixel stack into a cell-by-marker table via a segmentation mask and mean-intensity extraction, and that table has thrown away three things only the image still contains: whether the mask boundary matches a real cell, where a marker's signal physically sits (its spillover provenance), and morphology/context. None of these surface as an outlier in the table -- a merged CD3+CD20+ "cell" looks like a plausible rare double-positive, not an error -- so the table is seductive precisely because it is tidy and statistical. Annotation is the only QC step that lets pixels veto the table. The expert habit is distrust of any cell-table claim (a cluster, a double-positive population, a rare type) until it has been seen on the image with its mask overlaid. Three concrete bridge operations follow: overlay the mask on the DNA + summed-membrane channels and walk the image (the irreplaceable segmentation QC); paint clusters back onto tissue, where a real cluster forms coherent structures (a tumor nest, a T-cell zone) and an artifact cluster scatters as salt-and-pepper haze along the boundary between two real clusters (spatial incoherence is the tell); and gate in image space, not only expression space, because a biaxial gate drawn on the table is a thresholding decision made blind to the pixels. Two hard limits frame all of it: manual labels are not ground truth (expert-vs-expert concordance is only ~86%, and a substantial share of disagreements reflect genuinely ambiguous cells, so chasing >90% classifier accuracy is chasing noise above human agreement), and the display contrast limit IS a positivity threshold, so auto-scaling per image silently moves what counts as "positive" field to field.

Viewer Landscape

Tool	Stack	What it is for
napari + napari-imc	Python	open raw .mcd, overlay channels + masks + annotation layers, scriptable
napari-steinpose	napari plugin	human-in-the-loop Cellpose segmentation inside napari
Mantis Viewer	Electron (CANDELbio/Parker)	dedicated ground-truth label + region/population curation on huge images
cytomapper / cytomapperShiny	R/Bioconductor	gate on up to ~24 markers and see the gated cells painted on tissue
cytoviewer	R/Bioconductor	interactive image + mask overlay colored by cell metadata
TissUUmaps 3	browser/WebGL	whole-slide QC of marker/point overlays at 10^7+ points
QuPath	Java	whole-slide pathology; map clusters/cells back to tissue for validation

Decision Tree by Scenario

Task	Tool	Why
Open and inspect a raw .mcd	napari + napari-imc	only it reads IMC in machine coordinates
Generate ground-truth labels for a classifier	Mantis Viewer or napari points	built for population curation across large images
Gate a population and confirm it on tissue	cytomapperShiny	the gate-then-see-on-tissue loop
Whole-slide point-overlay QC	TissUUmaps	scales to 10^7 points
Segmentation mask QC/edit	napari add_labels / napari-steinpose	paint/fill integer masks
Confirm a cluster is real	paint cluster back on tissue (any viewer)	spatial incoherence = artifact

Open Raw IMC and Overlay the Mask

Goal: Put the raw channels, the segmentation mask, and an annotation layer in one canvas.

Approach: Use napari-imc for the .mcd, overlay the mask outlines on DNA + a summed membrane channel, and add a labels or points layer for annotation. Fix the contrast limits explicitly so "positive" means the same thing across fields.

import napari

viewer = napari.Viewer()
viewer.open('slide.mcd', plugin='napari-imc')          # reads acquisitions + panoramas
viewer.add_labels(cell_masks, name='masks')            # outlines to audit boundaries
# fix contrast (a display limit IS a positivity threshold) -- record it in the protocol
viewer.add_image(dna, name='DNA', contrast_limits=[0, 20], colormap='gray', blending='additive')
napari.run()

Paint Clusters Back onto Tissue

Goal: Decide whether a data-driven cluster is real biology or an artifact.

Approach: Color the mask by cluster and look: a real cluster forms coherent spatial structures; an artifact cluster scatters along the boundary between two real clusters.

import numpy as np

def cluster_label_image(masks, cell_ids, cluster_of_cell):
    # paint each cell's cluster id back onto its mask; view in napari and demand spatial
    # coherence (nests/zones/sheets). Salt-and-pepper haze along a boundary = artifact.
    out = np.zeros_like(masks)
    lut = dict(zip(cell_ids, cluster_of_cell))
    for cid, cl in lut.items():
        out[masks == cid] = cl + 1
    return out

Build a Class-Balanced Ground-Truth Set

Goal: Produce a training set that learns rare types and generalizes across batches.

Approach: Tissue is wildly imbalanced (hundreds vs tens-of-thousands of cells per class), so deliberately over-sample rare types when choosing what to label, and spread annotation across multiple patients/compartments -- label breadth beats label depth. Aim for hundreds-to-low-thousands of confident cells per class.

import numpy as np

def sample_cells_to_annotate(cell_ids, cell_types, per_class=300, rng=None):
    # over-sample rare classes toward a target count; annotating random fields lets rare
    # types stay unlearnably sparse
    rng = rng or np.random.default_rng(0)
    picks = []
    for ct in np.unique(cell_types):
        pool = cell_ids[cell_types == ct]
        picks.append(rng.choice(pool, size=min(per_class, len(pool)), replace=False))
    return np.concatenate(picks)

Per-Trap Failure Modes

Trusting a table double-positive

Trigger: a CD3+CD20+ population from the cell table. Mechanism: under-segmentation or lateral spillover makes a chimeric vector that looks like a plausible rare type. Symptom: a "novel doublet lineage". Fix: overlay mask + both channels on those exact cells; two abutting nuclei means a segmentation artifact.

Per-image auto-contrast while annotating

Trigger: letting the viewer auto-scale each field. Mechanism: the contrast limit is a positivity threshold; auto-scaling moves it per field. Symptom: "positive" drifts image to image; inconsistent labels. Fix: fix and record contrast limits; apply the same transform to every annotator and image.

Chasing >90% classifier accuracy

Trigger: treating manual labels as ground truth. Mechanism: expert-vs-expert concordance is ~86%, and many disagreements reflect genuinely ambiguous cells. Symptom: overfitting to one annotator's noise. Fix: use multi-annotator consensus for the evaluation set; report inter-annotator agreement as the ceiling.

Annotating one ROI deeply

Trigger: labeling many cells in a single field. Mechanism: batch/staining variation between images is a top failure mode. Symptom: the classifier generalizes only to that ROI. Fix: spread annotation across patients/images and compartments; breadth over depth.

Quantitative Thresholds

Threshold	Source	Rationale
Inter-annotator concordance ~86%	Amitay 2023 Nat Commun 14:4302	the realistic accuracy ceiling; many disagreements are genuinely ambiguous
Hundreds-to-low-thousands confident cells/class	Amitay 2023; Shaban 2024	enough to train; rare classes and inter-image variation bind, not total count
Over-sample rare classes + Poisson-resample augmentation	Amitay 2023 Nat Commun 14:4302	tissue is imbalanced; signal is ion counts
Labels NOT harvested from clustering	annotation hygiene	clustering-derived labels re-import the double-positive artifact

Common Errors

Error / symptom	Cause	Solution
.mcd will not open in napari	core napari has no IMC reader	install and use the napari-imc plugin
Annotation layer behaves unexpectedly	wrong layer type	add_labels for masks, add_points (with features) for per-cell labels
Cluster looks tight but is biologically odd	spillover/segmentation artifact	paint it on tissue; demand spatial coherence
Rare cell type unlearnable	random-field annotation	deliberately over-sample rare types; augment
Classifier plateaus below expectation	exceeding inter-annotator agreement	accept the ~86% ceiling; consensus-label the evaluation set

References

• napari contributors. 2019. napari: a multi-dimensional image viewer for Python. Zenodo. doi:10.5281/zenodo.3555620. — no journal paper exists; cite the Zenodo DOI.
• Amitay Y, Bussi Y, Feinstein B, Bagon S, Milo I, Keren L. 2023. CellSighter: a neural network to classify cells in highly multiplexed images. Nat Commun 14:4302. — inter-annotator concordance; ground-truth and augmentation.
• Shaban M, et al. 2024. MAPS: pathologist-level cell type annotation from tissue images through machine learning. Nat Commun 15:28. — annotation scale and class imbalance.
• Geuenich MJ, Hou J, Lee S, et al. 2021. Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data. Cell Syst 12(12):1173-1186.e5. — marker-prior labels as expert annotation.
• Bankhead P, Loughrey MB, Fernandez JA, et al. 2017. QuPath: Open source software for digital pathology image analysis. Sci Rep 7:16878. — whole-slide validation.
• Pielawski N, Andersson A, Avenel C, et al. 2023. TissUUmaps 3: Improvements in interactive visualization, exploration, and quality assessment of large-scale spatial omics data. Heliyon 9(5):e15306. — whole-slide point QC.
• Chevrier S, Crowell HL, Zanotelli VRT, et al. 2018. Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry. Cell Syst 6(5):612-620.e5. — channel spillover (distinct from lateral spillover caught visually).

Related Skills

• cell-segmentation - mask overlay is the irreplaceable segmentation QC
• phenotyping - annotation supplies labels/priors and confirms clusters are real
• quality-metrics - the image catches artifacts that table statistics cannot
• data-preprocessing - contrast/transform choices mirror preprocessing thresholds
• spatial-analysis - spatial coherence of a painted cluster validates it