Automated Masking of Voltage-Sensitive Dye Imaging Data
Bibliographic record
Abstract
This paper discusses the development of an algorithm to mask poor quality data in fluorescence videos of cardiac tissue stained with voltage-sensitive dye. The aim was to simplify further analysis by eliminating the step of manually masking areas of poor signal quality and areas outside the preparation of interest. Our algorithm estimates signal to noise ratio (SNR) from the power spectral density (PSD) for each pixel. This information is combined with information about the fluorescence intensity in each pixel, according to a user-selectable weighting factor. A threshold is then applied to the resulting combined measure. This approach resulted in an effective algorithm that is capable of automatically creating a "mask" that can be applied to the data to exclude parts of the data from further analysis. The algorithm is sufficiently efficient to allow interactive use, allowing the user to adjust the parameters of the algorithm and instantly view the resulting mask. This tool will be useful as a technique to simplify further analysis of voltage-sensitive dye imaging data.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| 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".