A Novel 3-D Image-Based Morphological Method for Phenotypic Analysis
Bibliographic record
Abstract
A new approach for the study of geometric morphometrics is presented based on well-established image processing techniques in a novel combination to support high-throughput analysis necessary for large-scale determination of genotype-phenotype relationships. The method retains full 3-D data, and avoids manual landmark selection. Micro-computed tomography images are superimposed into a common orientation by rigid image registration with an isotropic scale factor. An average sample shape is determined by averaging the intensities of corresponding voxels of the registered images, and shape variation is determined by calculating the image gradient of the average shape. Localized shape differences between mean images or between an individual and a group mean are identified and quantified by surface-to-surface distance measures of superimposed images. Validation was performed using geometric shapes of known dimensions as well as biological samples of C57 BL/6 J and A/WySnJ mouse skulls, and shape variation of the mouse skulls was consistent with previously published results. Although the image gradient is sensitive to both image registration and filtration of the average image, the effect can be minimized by consistent use of image analysis parameters. While the proposed approach deviates from well-established landmark-based geometric morphometric tools, it is not intended to replace these current methods. Rather, it will be an important contribution to provide high-throughput screening in large-scale studies focused on understanding genotype-phenotype relationships so that subsequent morphometric approaches using established techniques can be better focused.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".