FAST: Filamentous Actin Segmentation Tool for quantifying cytoskeletal organization
Bibliographic record
Abstract
Abstract Studying how actin filaments are assembled into different subcellular structures can provide insights into both physiological processes and the mechanisms of disease. However, quantifying the size, abundance, and organization of different classes of actin structure from optical microscopy data remains a challenge. To address this, we developed a deep learning based Filamentous Actin Segmentation Tool (FAST) to accurately and efficiently segment and quantify different classes of actin structure from phalloidin stained confocal microscopy images. We evaluated the performance of this tool to segment and quantify the abundance of different classes of actin structure in different cell lines and with dynamic changes in actin organization using lifeact-GFP during drug treatments. FAST enables quantification of different classes of actin structure from actin images alone, without the need for specific antibodies against proteins in different actin structures and hence can be a useful tool for researchers studying for studying actin related pathways involved in cell motility, cancer metastasis, and drug development. Summary Statement We developed Filamentous Actin Segmentation Tool (FAST), that leverages deep learning and antibody assisted labeling to segment and quantify actin structures from optical microscopy images.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.007 | 0.003 |
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".