Mapping Microvascular Network Geometry in 3D
Bibliographic record
Abstract
The objective is to develop a novel mapping software package using MATLAB to reconstruct microvessels in 3‐dimensions for use in oxygen transport modeling schemes. A single optical imaging system is used to collect experimental in vivo data (hemodynamics and oxygen saturation). Vessels are selected from video sequences and functional still images using automated edge tracking and depth information, collected during the experiment, to construct the 3D network. Simple user driven commands allow the connection of vessel segments and the creation of bifurcations. Built in registration and calibration improves accuracy of relative vessel position and allows vessels to span across multiple focal planes and fields of view. Resulting output provides a simple and versatile array that accurately describes 3D network geometry. The final network visualization (see figure) represents vessels as variable diameter tubes that are scaled accurate to the bounding volume. Networks can be rotated and manipulated in 3D to verify network connections and vessel continuity. The network and corresponding hemodynamic and SaO 2 data can then be easily integrated into computational oxygen transport models.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".