Reconstruction of the Cerebral Ventricular System System in Stereoscopy
Bibliographic record
Abstract
The complexity of the human cerebral ventricular system, coupled with its deep position within the brain, may pose a problem for conceptualizing its anatomy. Cadaveric dissection may be inadequate for learning the anatomy of the cerebral ventricular system as ventricular structures represent spaces rather than physical structures. Three‐dimensional (3D) computer reconstruction of the ventricular system may offer a solution. This study aims to create an accurate 3D computer reconstruction of the ventricular system with surrounding structures from 4T magnetic resonance (MR) scans of a male cadaver. Using 3D segmentation and rendering software, structures of interest were reconstructed through manual segmentation. The finished model can be manipulated by adding or removing structures, altering opacity, or taking slices through the model to highlight inner structures. Complex physiologic concepts, such as cerebrospinal fluid flow, can be shown using the 3D model through a video animation. Stereoscopic projection can be used increase depth perception and emphasize spatial relationships between anatomical structures. This model is suited for both self‐directed learning and classroom teaching of 3D anatomical structure and spatial orientation of the ventricles, their connections, and their relation to adjacent neural and skeletal structures. Research support: Ontario Graduate Scholarship. Grant Funding Source Internal
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".