Photogrammetry or 3D Surface Scanning – Which tool works best for anatomical specimens?
Bibliographic record
Abstract
High fidelity 3D representations of anatomy are becoming increasingly popular as educators capture anatomical specimens for use in virtual and augmented reality applications or for providing a 3D representation that can be freely manipulated in web applications. The question then arises which tool best suits the task at hand ‐ both 3D scanning and photogrammetry are options. We have compared the use case for these technologies in medical education ‐ our aim was to create 3D models of anatomical specimens with high quality and resolution. Various qualitative and quantitative criteria were applied to determine and compare the performance fidelity and results of 3D scanning with an Artec Space Spider versus photogrammetry using a standard DSL camera and Agisoft PhotoScan (PS) Standard Edition. We compared the basic principles of setup and use of both technologies – while photogrammetry requires accurate lighting and some rigging for the camera to provide consistent results, the 3D surface scanner is handheld and since it acts as its own light source the scanning results are independent of environmental factors. We found that the workflow and technique for best use of these technologies varies and the investment in these techniques depends on the needs of the user. We established protocols and setting for both techniques to standardize and streamline the data acquisition process. Each technology has advantages in some metrics and disadvantages in others. While photogrammetry provided the better surface textures, 3D surface scanning provided more accurate 3D geometries in particular in those specimens that were more complex or had highly reflective surfaces. We also found that 3D surface scanning was able to more accurately capture the geometries of softer specimens that may distort as they are handled. Overall, both technologies yield excellent results for the use in anatomical sciences education. The decision about which technology to invest in depends on the characteristics of the specimens that need to be scanned as well as the environmental parameters. Support or Funding Information Supported by the Strategic Investment Fund of the UBC Faculty of Medicine This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".