ArticleThe journal of histochemistry and cytochemistry : official journal of the Histochemistry Society2014
New automated single-cell technique for segmentation and quantitation of lipid droplets.
Article in The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Lipid droplet size profiling in yeast.Biology open · 2026Article
- Artificial Intelligence for Organelle Segmentation in Live-Cell Imaging.Research (Washington, D.C.) · 2025Review
- Non-invasive label-free imaging analysis pipeline for in situ characterization of 3D brain organoids.Scientific reports · 2024Article
- Non-Invasive Label-free Analysis Pipeline for In Situ Characterization of Differentiation in 3D Brain Organoid Models.Research square · 2024Article
- Label-free third harmonic generation imaging and quantification of lipid droplets in live filamentous fungi.Scientific reports · 2022Article
- How to follow lipid droplets dynamics during adipocyte metabolism.Journal of cellular physiology · 2022Article
- Image Segmentation and Quantification of Droplet dPCR Based on Thermal Bubble Printing Technology.Sensors (Basel, Switzerland) · 2022Article
- Article
- Lipid droplet quantification based on iterative image processing.Journal of lipid research · 2019Article
- Quantitative imaging of lipid droplets in single cells.The Analyst · 2019Review
- New Method for Quantitation of Lipid Droplet Volume From Light Microscopic Images With an Application to Determination of PAT Protein Density on the Droplet Surface.The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society · 2018Article
- Methods for Lipid Droplet Biophysical Characterization inFrontiers in microbiology · 2018Review
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2 authors.
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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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