New Automated Single-Cell Technique for Segmentation and Quantitation of Lipid Droplets
Bibliographic record
Abstract
Lipid droplets are the major organelle for intracellular storage of triglycerides and cholesterol esters. Various methods have been attempted for automated quantitation of fluorescently stained lipid droplets using either thresholding or watershed methods. We find that thresholding methods deal poorly with clusters of lipid droplets, whereas watershed methods require a smoothing step that must be optimized to remove image noise. We describe here a novel three-stage hybrid method for automated segmentation and quantitation of lipid droplets. In this method, objects are initially identified by thresholding. They are then tested for circularity to distinguish single lipid droplets from clusters. Clusters are subjected to a secondary watershed segmentation. We provide a characterization of this method in simulated images. Additionally, we apply this method to images of fixed cells containing stained lipid droplets and GFP-tagged proteins to provide a proof-of-principle that this method can be used for colocalization studies. The circularity measure can additionally prove useful for the identification of inappropriate segmentation in an automated way; for example, of non-cellular material. We will make the programs and source code available to the community under the Gnu Public License. We believe this technique will be of interest to cell biologists for light microscopic studies of lipid droplet biology.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".