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H01 electron microscopy with object segmentation and original 2 µm scale bar Nanoscale · Module 06

Segmentation 101

Teaching Deck

Human cortex · H01
Object segmentation over electron microscopy

H01 release · Lichtman Lab / Harvard & Connectomics at Google · CC BY 4.0
Shapson-Coe et al. (2024) · doi:10.1126/science.adk4858

Module 06 · Nanoscale

Learning Objectives

  • Explain segmentation goals and constraints
  • Identify merges, splits, and boundary errors
  • Apply a basic correction workflow
  • Report segmentation quality with clear metrics
Module 06 · Nanoscale

Capability Target

Detect and categorize core segmentation errors and execute one correction cycle with documented quality impact.

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Concept Focus

1) What segmentation does and why it matters

Segmentation is the computational process of assigning every voxel in an EM volume to a specific object — not just "this is a neuron" but "this is neuron #47,293." This is instance segmentation, and it's the foundation of the entire connectome. Without accurate segmentation, you cannot identify individual neurons, trace their morphology, or determine their synaptic connections. Modern methods (flood-filling networks, U-Net + watershed + agglomeration) achieve "superhuman" accuracy on benchmarks but still make errors at rates that compound across large volumes.

Module 06 · Nanoscale

Core Workflow

  • Load segmented patch in Neuroglancer or equivalent viewer.
  • Identify merge/split candidates by scrolling through z and checking 3D meshes for implausible morphology.
  • Apply correction: split merged segments at the boundary, merge split fragments by verifying continuity.
  • Recalculate quality indicators: did the correction improve local metrics?
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Core Workflow (continued)

  • Log decisions: record what was changed, why, and what evidence supported the decision.
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Run of Show (60 min)

  • 00:00-08:00 | Segmentation goals
  • 08:00-22:00 | Error taxonomy with real examples
  • 22:00-36:00 | Guided correction round
  • 36:00-48:00 | Quality metric interpretation
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Run of Show (60 min) (continued)

  • 48:00-60:00 | Debrief and competency check
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Misconceptions to Watch

  • Misconception guardrail: merge and split errors are equally costly, so error counts alone rank corrections.
  • Misconception guardrail: an object that looks like a plausible neuron is evidence that the segmentation is correct.
  • Misconception guardrail: the most visually obvious errors are the ones most worth fixing.
  • Misconception guardrail: a segmentation can be finished, rather than released at a stated level with stated remaining error.
Module 06 · Nanoscale

A segmentation is a claim about continuity

The same field, before and after model labels are overlaid.

Raw H01 image at the same field and scale as the labeled view.
Underlying image
The same H01 field with object colors overlaid and the 2 micrometre scale bar intact.
Model object labels

Discuss: What evidence would you need before calling a merge or split?

H01 release, Lichtman Lab / Harvard and Connectomics at Google; Shapson-Coe et al. (2024), doi:10.1126/science.adk4858; CC BY 4.0.

Module 06 · Nanoscale

Two reconstruction choices can disagree

Inspect local evidence before choosing a correction.

Two panels of the same H01 region: c2 assigns one object; c3 separates the highlighted regions into two.
Left: c2, one object. Right: c3, two objects.

Discuss: Which additional view would help adjudicate this difference?

H01 release, Lichtman Lab / Harvard and Connectomics at Google; Shapson-Coe et al. (2024), doi:10.1126/science.adk4858; CC BY 4.0.

Module 06 · Nanoscale

Studio Activity

Scenario: Your team has a freshly segmented 50x50x50 um subvolume containing approximately 200 neuron fragments. Automated error detection has flagged 25 candidate errors. You have time to fix 10.

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Activity Output Checklist

  • Evidence-linked artifact submitted.
  • At least one limitation or uncertainty stated.
  • Revision point captured from feedback.
Module 06 · Nanoscale

Assessment Rubric

Minimum pass

  • Every flagged candidate carries an error-type label (merge/split/boundary/uncertain), and mislabels affect fewer than 3 of the 25 candidates.
  • At least one correction is executed with the supporting evidence stated in the log before the edit, not reconstructed afterward.
  • The correction log records object ID, operation, location, and the specific visual evidence — a reader could re-find the site from the log alone.
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Assessment Rubric (continued)

  • Before/after quality indicators are computed for at least one correction, with the direction of change stated.
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Assessment Rubric

Strong performance

  • The priority ranking ties each candidate to its expected effect on the downstream connectivity graph, and merges outrank splits of comparable size with the reason stated.
  • Before/after metrics are reported for the whole subvolume, and any metric that moved the wrong way is explained rather than omitted.
  • At least one flagged candidate is explicitly deferred with a recorded reason (cost to fix, ambiguity, outside analysis set) rather than silently skipped.
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Assessment Rubric (continued)

  • The release note states what remains unexamined and what error types are still expected, not only what was fixed.
Module 06 · Nanoscale

Assessment Rubric

Common failure to flag

  • Correction without evidence of quality change — fixing things without checking whether it helped.
  • Ranking by visual conspicuousness rather than by impact on the analysis.
  • A merge introduced while fixing a split, because continuity was assumed from appearance instead of verified across sections.
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Exit Ticket

Explain when you would defer a correction instead of fixing immediately.

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References (Instructor)

  • Use module references listed on the module page.
Module 06 · Nanoscale

Teaching Materials

  • Module page: /modules/module06/
  • Session kit: /teaching/sessions/module06/
  • Worksheet: /assets/worksheets/module06/module06-activity.md
Module 06 · Nanoscale

Read, review, or teach

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The reading version includes figure descriptions and discussion guidance.

Nanoscale text: CC BY 4.0. Scientific figures retain their individual credits.

Module 06 · Nanoscale

Pre-class preparation (10 min async) Read the error taxonomy content library entry Open the practice dataset in Neuroglancer and browse for 5 minutes Minute-by-minute plan 00:00-08:00 | Segmentation goals "What would a perfect segmentation look like? Every neuron correctly labeled, every membrane correctly placed." Show a well-segmented region side-by-side with raw EM. Point out: each color = one neuron. Then show the same region with errors highlighted. "This is reality. Our job is to find and fix these." 08:00-22:00 | Error taxonomy with real examples Walk through one merge error: show the 3D mesh with impossible branching, navigate to the merge point in 2D slices, explain why the model failed (low contrast at a blood vessel). Walk through one split error: show a dead-end axon fragment, then the continuation 3 sections later. Explain: thin process + poor contrast = model lost it. Walk through one boundary error: show a synapse attributed to the wrong neuron because the membrane position is off by 2 pixels. For each: "What would this error do to your analysis?" 22:00-36:00 | Guided correction round Learners work in pairs on 3 pre-identified errors (1 merge, 1 split, 1 ambiguous). Instructor circulates, coaching on: "Show me the evidence before you correct." "What would happen if this merge is actually correct — two branches of the same neuron?" 36:00-48:00 | Quality metric interpretation Introduce: "How do we know if our corrections actually helped?" Brief overview of metrics: edge precision/recall (are the connections right?), segment size distributions (do sizes look biological after correction?). Compute metrics before and after the correction round. Did they improve? 48:00-60:00 | Debrief and competency check Each learner presents one correction with evidence chain. Group discussion: "Which correction had the biggest impact on the graph? Why?" Exit ticket: "Name the error type you found hardest to detect and why."