Neuron Morphology and Skeletonization

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Neuron Morphology and Skeletonization

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

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Learning Objectives

• Generate skeleton representations from reconstructed neurites
• Compute core morphology descriptors
• Relate morphology metrics to biological interpretation
• Report morphology uncertainty and classification limits

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Capability Target

Produce a skeleton-based morphology summary with at least three descriptors and one explicit limitation.

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Concept Focus

1) What is skeletonization and why do we need it?

A segmented neuron occupies millions of voxels in the EM volume. To analyze its morphology efficiently, we reduce it to a skeleton: a tree graph where nodes represent points along the neurite centerline and edges represent the path between them. Skeletons compress a neuron's 3D structure from gigabytes to kilobytes while preserving topology (branching pattern, path lengths, connectivity) — at the cost of surface geometry and spine shape, which are discarded.

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Core Workflow

• Build skeleton from volumetric segmentation using TEASAR or equivalent algorithm.
• Quality-check the skeleton: prune spurious branches, verify branch points, check for disconnected fragments.
• Compute descriptors: cable length, branch points, Strahler number, Sholl profile, spine density.
• Compare against reference patterns: does this neuron match the expected morphology for its putative cell type?

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Core Workflow (continued)

• Report interpretation confidence: which descriptors are robust, which are affected by reconstruction quality?

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Run of Show (60 min)

• 00:00-10:00 | Morphology overview
• 10:00-24:00 | Skeleton extraction demo
• 24:00-38:00 | Descriptor calculation
• 38:00-50:00 | Interpretation and caveats

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Run of Show (60 min) (continued)

• 50:00-60:00 | Competency check

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Misconceptions to Watch

• Misconception guardrail: a skeleton is a lossless summary of a neuron.
• Misconception guardrail: morphological measurements are comparable across cells that were proofread to different levels.
• Misconception guardrail: total dendritic length is a property of the neuron itself, independent of how it was reconstructed.
• Misconception guardrail: a cell type assigned from morphology alone needs no corroboration from connectivity or molecular identity.

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A skeleton makes measurement choices explicit

Synthetic planar tree. Coordinates are in micrometres.

Discuss: Compute total cable length and both root-to-tip path lengths.

Synthetic fixture; exact geometry checked by scripts/check_visual_design.rb.

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A missing branch changes the story

Use the synthetic tree on the previous slide.

1. 1

   Complete example

   Three edges: total length 11 µm.

2. 2

   Truncated example

   Remove B–D: measured length is 7 µm.

3. 3

   Report the limit

   The lower value describes the observed reconstruction.

Discuss: Does a lower measurement prove a smaller biological arbor?

Illustrative teaching scenario; see this module’s lesson and worked example.

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Studio Activity

Scenario: You have skeletons for 10 neurons in L2/3 of mouse visual cortex. Your task is to classify them as pyramidal vs interneuron based on morphology alone, then validate against synapse-based classification (excitatory vs inhibitory output synapses).

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Activity Output Checklist

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

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Assessment Rubric

Minimum pass

• Valid skeleton and descriptor set for all 10 neurons, with at least 3 descriptors each.
• Every classification carries a stated evidence chain, not just a label.
• At least one explicit measurement limitation named, tied to a specific reconstruction issue.

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Assessment Rubric

Strong performance

• Descriptors partitioned into robust versus reconstruction-sensitive for each borderline cell.
• Mismatches between morphological and synapse-based calls traced to a root cause (truncation, split, spurious branches) rather than logged as disagreement.
• Per-cell completeness estimated and reported next to every absolute measurement.

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Assessment Rubric (continued)

• Ratios computed on contained compartments wherever truncation is present.

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Assessment Rubric

Common failure to flag

• Descriptor list without biological context — numbers with no statement of what they mean for identity.
• Classification from a single descriptor when the others disagree.
• Absolute cable length or arbor volume reported for truncated cells without a lower-bound qualifier.

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Exit Ticket

Explain one morphology feature that could be confounded by reconstruction quality.

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References (Instructor)

• Use module references listed on the module page.

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Teaching Materials

• Module page: /modules/module09/
• Session kit: /teaching/sessions/module09/
• Worksheet: /assets/worksheets/module09/module09-activity.md

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