Session Kit: Module 06: Segmentation 101
Everything needed to run Module 06 as a taught session: prep, timing, materials, misconceptions, rubric.
Generated from modules/module06.md. Edit the module page, not this file.
At a glance
| Duration | 4 hours |
| Capability target | Detect and categorize core segmentation errors and execute one correction cycle with documented quality impact. |
| Learners leave with | Ranked error list with type classifications and impact estimates |
Before you walk in
- You can state the capability target in one sentence without reading it.
- You have one worked example you will narrate, including where you are unsure.
- Data access works — accounts, viewer, notebook — verified today, not last week.
- The rubric is visible to learners before they start, not after.
- You have decided what “uncertain” earns, and you will say so out loud.
Learners should arrive having covered:
- Modules 01-05
Materials
Run of show
| Time | Segment | Your note |
|---|---|---|
| 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 | |
| 48:00-60:00 | Debrief and competency check |
The 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.
- Review all 25 flagged candidates and classify each by error type (merge/split/boundary/uncertain).
- Rank by estimated impact: which corrections would most change the connectivity graph?
- Fix the top 10 in priority order, documenting each correction.
- Compute before/after metrics for the subvolume.
- Write a 3-sentence “release note” summarizing what was fixed and what remains.
What learners hand in
- Ranked error list with type classifications and impact estimates
- Correction log with before/after evidence for each fix
- Metric summary table
- Release note
Misconceptions to target
These are the errors this session exists to prevent. Surface them in the debrief rather than pre-empting them in the lecture — a misconception a learner has voiced is far easier to correct than one they are holding silently.
- They may believe: Merge and split errors are equally costly, so error counts alone rank corrections.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: An object that looks like a plausible neuron is evidence that the segmentation is correct.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: The most visually obvious errors are the ones most worth fixing.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: A segmentation can be finished, rather than released at a stated level with stated remaining error.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
Naming the norm
Every session is a chance to make one piece of the hidden curriculum explicit. Pick a moment where you would normally just do the professional thing, and say out loud why you are doing it — then ask whether anyone was taught that.
For this session, the candidate is whichever norm the activity most depends on: stating an assumption in the same sentence as the claim, recording the version a number came from, or saying “uncertain” and having it count as a real answer. See the hidden curriculum for the collected set and why naming them is a fairness intervention rather than etiquette.
Assessment
- Minimum pass: Correct error labels and at least one valid correction with evidence.
- Strong performance: Correction prioritization explicitly tied to downstream analysis impact. Metrics show measurable improvement.
- Common failure to flag: Correction without evidence of quality change — fixing things without checking whether it helped.
Grade the reasoning, not the answer. A correct call with no evidence chain should not outscore a well-reasoned incorrect one — and saying so publicly changes behaviour within one session.
Exit prompt
Explain when you would defer a correction instead of fixing immediately.
If this session goes wrong
- Nobody talks in the debrief. You asked “any questions?” Ask instead: “Which cue would you drop first if the data got worse?”
- Everyone finishes early. They are pattern-matching, not judging. Give an ambiguous case where the answer is “uncertain” and see what happens.
- Nobody finishes. The scaffolding came off too fast. Work the next case together rather than pressing on.
- A learner is silently lost. The most likely cause is unstated vocabulary. Point them at the dictionary and check back.