Virtual cerebral ventricular system: An MR‐based three‐dimensional computer model
Bibliographic record
Abstract
The inherent spatial complexity of the human cerebral ventricular system, coupled with its deep position within the brain, poses a problem for conceptualizing its anatomy. Cadaveric dissection, while considered the gold standard of anatomical learning, may be inadequate for learning the anatomy of the cerebral ventricular system; even with intricate dissection, ventricular structures remain difficult to observe. Three-dimensional (3D) computer reconstruction of the ventricular system offers a solution to this problem. This study aims to create an accurate 3D computer reconstruction of the ventricular system with surrounding structures, including the brain and cerebellum, using commercially available 3D rendering software. Magnetic resonance imaging (MRI) scans of a male cadaver were segmented using both semiautomatic and manual tools. Segmentation involves separating voxels of different grayscale values to highlight specific neural structures. User controls enable adding or removing of structures, altering their opacity, and making cross-sectional slices through the model to highlight inner structures. Complex physiologic concepts, such as the flow of cerebrospinal fluid, are also shown using the 3D model of the ventricular system through a video animation. The model can be projected stereoscopically, to increase depth perception and to emphasize spatial relationships between anatomical structures. This model is suited for both self-directed learning and classroom teaching of the 3D anatomical structure and spatial orientation of the ventricles, their connections, and their relation to adjacent neural and skeletal structures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".