Average Cell Orientation, Shape and Size Estimated from Tissue Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Four computer vision algorithms to measure the average orientation, shape and size of cells in images of biological tissue are proposed and tested. These properties, which can be embodied by an elliptical 'composite cell' are crucial for biomechanical tissue models. To automatically determine these properties is challenging due to the diverse nature of the image data, with tremendous and unpredictable variability in illumination, cell pigmentation, cell shape, and cell boundary visibility. First, a simple edge detection routine is performed on the raw images to locate cell edges and remove pigmentation variation. The edge map is then converted into the magnitude spatial-frequency domain where the spatial patterns of the cells appear as energy impulses. Four candidate methods that analyze the spatial-frequency data to estimate the properties of the composite cell are presented and compared. These methods are: least squares ellipse fitting, correlation, area moments and Gabor filters. Robustness is demonstrated by successful application on a wide variety of real images.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it