<title>3D reconstruction of a human heart fascicle using SurfDriver</title>
Bibliographic record
Abstract
The Temple University Medical School has a sequence of over 400 serial sections of adult normal ventricular human heart tissue, cut at 25 micrometer thickness. We used a Zeiss Ultraphot with a 4x planapo objective and a Pixera digital camera to make a series of 45 sequential montages to use in the 3D reconstruction of a fascicle (muscle bundle). We wrote custom software to merge 4 smaller image fields from each section into one composite image. We used SurfDriver software, developed by Scott Lozanoff of the University of Hawaii and David Moody of the University of Alberta, for registration, object boundary identification, and 3D surface reconstruction. We used an Epson Stylus Color 900 printer to get photo-quality prints. We describe the challenge and our solution to the following problems: image acquisition and digitization, image merge, alignment and registration, boundary identification, 3D surface reconstruction, 3D visualization and orientation, snapshot, and photo-quality prints.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.018 |
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".