Module 14: Computer Vision for EM

Teaching Deck

Learning Objectives

• Explain how classical and deep CV methods map to connectomics tasks
• Compare model outputs using biologically meaningful error criteria
• Design a validation plan for CV pipelines in EM data
• Report CV limitations with reproducibility safeguards

Session Outcomes

• Learners can complete the module capability target.
• Learners can produce one evidence-backed artifact.
• Learners can state one limitation or uncertainty.

Agenda (60 min)

• 0-10 min: Frame and model
• 10-35 min: Guided practice
• 35-50 min: Debrief and misconception correction
• 50-60 min: Competency check + exit ticket

Capability Target

Design and evaluate a CV pipeline for EM imagery that is fit for a specific connectomics task and explicitly bounded by known failure modes.

Concept Focus

1) Task-model fit

• Technical: detection, segmentation, denoising, and classification tasks require different objective functions and architectures.
• Plain language: pick models for the job, not by popularity.
• Misconception guardrail: one model can solve all EM tasks equally well.

Core Workflow

• Define EM task and acceptable error envelope.
• Select baseline and candidate CV approaches.
• Run evaluation using biologically relevant metrics.
• Perform failure-case review on ambiguous regions.
• Publish model card with limitations and intended use.

60-Minute Run-of-Show

• 00:00-08:00 task framing + exemplar failure modes.
• 08:00-20:00 choose metrics tied to downstream biology.
• 20:00-34:00 evaluate baseline vs candidate model.
• 34:00-46:00 error taxonomy and triage discussion.
• 46:00-56:00 model card drafting.
• 56:00-60:00 competency check.

Misconceptions to Watch

• Misconception guardrail: one model can solve all EM tasks equally well.
• Misconception guardrail: high benchmark score implies safe downstream use.
• Misconception guardrail: visual plausibility is sufficient validation.

Studio Activity

Scenario: Compare two segmentation-support CV models for an EM subvolume.

Activity Output Checklist

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

Assessment Rubric

• Minimum pass: clear task-model rationale, biologically relevant metrics, explicit limitations.
• Strong performance: robust failure analysis and operational release criteria.
• Failure modes: metric-only reasoning, weak split design, no deployment boundaries.

Exit Ticket

Document one CV result with one supported use case and one forbidden use case.

References (Instructor)

• Januszewski et al. (2018) for segmentation model context.
• Recent MICrONS/FlyWire methods for practical CV constraints.

Teaching Materials

• Module page: /modules/module14/
• Slide page: /modules/slides/module14/
• Worksheet: /assets/worksheets/module14/module14-activity.md