3D‐Recontruction of CNS Structures Derived From MRI Scans: New Teaching Tools for Neuroanatomy
Bibliographic record
Abstract
Neuroanatomy requires an understanding of 3D relationships of structures within the brain. This is often a challenge for students who try to visualize these structures from cross‐sectional anatomy or selected pro‐sections. The purpose of this project was to facilitate the 3D understanding of CNS structures through 3D reconstructions from brain MRI scans. 3D reconstructions of various CNS structures were made and posted as edited movies on www.neuroanatomy.ca . 3D reconstructions were based on MRI data sets obtained from a volunteer and from the Visible Human Project (NIH, National Library of Medicine). Structures were manually traced in coronal, sagittal, and transverse planes using AMIRA 4.1 software. The results of this project are available on a website designed for the neuroanatomy laboratory component. The following structures were completed: entire CNS structure, brain vasculature, limbic mamothalamic tracts, optic tracts, eye muscles, subcortical fibers, corpus callosum, thalami and basal ganglia, internal capsule, dentorubrothalamic tract, and spinal tracts. These new additions will allow students to study specific areas and structures of the CNS in 3D. We believe that these tools used in addition with traditional CNS models and specimens, will greatly enhance the spatial understanding of CNS structures for students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".