Using Focus Stacking in Anatomical Imaging: Does it Make a Difference?
Bibliographic record
Abstract
Human anatomy education relies heavily on the use of high‐quality images of cadaveric dissections for student reference. Despite continual advances in the available camera equipment for digital photography, depth of field remains a limitation when photographing anatomical specimens. To circumvent this issue, some anatomists use focus stacking algorithms to enhance depth of field in anatomical images. Focus stacking is a commonly used technique in microscopy whereby image processing software is used to fuse multiple digital images with different focal planes to create a single image with an extended depth of focus. This procedure aims to improve the quality of images used in anatomical education; however, objective evaluation of image quality is notoriously difficult. This is largely due to an inability to code algorithms that adequately parallel the human visual system (HVS). For this reason, subjective assessment has remained the gold standard for image quality evaluation. The goal of this study was to determine if focus stacked images were superior to non‐focus stacked images in subjective image quality. For this comparison, focus stacked cadaveric images were paired with identical non‐focus stacked controls (25 focus stacked, 25 non‐focus stacked) and analyzed for image quality by human subjects. Participants ranked the images on a slider labelled with five adjectives: “Bad”, “Poor”, “Fair”, “Good”, and “Excellent”, and scores were converted to integers between 1–100 for analysis. Statistical analysis is ongoing; however, preliminary results indicate that focus stacked images typically received higher ratings of quality than non‐focus stacked images. Ultimately, the purpose of capturing cadaveric images is to include them in educational material directed toward human anatomy students. Therefore, a difference between perceived image quality may have important implications for the creation of high quality educational material in human anatomy and student learning.
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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 | 0.026 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".