The best of each capture – the combination of 3D laser scanning with photogrammetry for optimized digital anatomy specimens.
Bibliographic record
Abstract
Visualization of anatomical specimens has gone through an evolution from drawings to photographs and now 3D volumetric capture of dissected specimens. The technologies behind this 3D volumetric visualization are rapidly evolving and becoming more broadly accessible with improved hardware and software. There are two main approaches to 3D reconstruction: laser scanning and photogrammetry (PGM). Our previous work demonstrated that while laser scanning provides superior geometric accuracy, PGM delivers a more photorealistic texture. For accurate anatomical study, both a precise geometry and a photo‐realistic texture are necessary ‐ so we set out to combine both methods. We found that both methods individually and the combination capture method work best with rigid (plastinated or bony) specimens – but many dissected specimens are wet specimens, which tend to change in their geometry depending on how they are positioned on a surface for capture. We created a methodology to provide consistency and support for the geometry of wet specimens with an alginate cast and a rigging method for longer pieces such as vessels and nerves. The cast allows us to flip the specimen with minimal disturbance in order to capture all sides accurately with both 3D laser scanning and PGM. The two datasets are then combined in Artec Studio and Reality Capture software packages. The combination of these two methods results in a very accurate 3D geometric mesh of the specimen with a photorealistic texture, providing the most realistic 3D volumetric captures of complex anatomical specimens. The combination method is more time‐consuming than each of the methods individually and a greater level of care must be taken to prevent any misalignment between the two datasets. Many specimens can be captured in an accurate and satisfactory way with either PGM or 3D laser scanning alone, and some will require this combination capture. The advantages and limitations of each method need to be compared to the desired outcome. Our approach results in superior capture quality, especially for non‐rigid specimens. The digitization of both anatomical and pathological specimens can improve accessibility and distribution of high quality digital specimens for both education and research. Both ID and CD contributed equally to this work.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".