Influence of software parameters on measurements in automatized image-based analysis of fat tissue histology.
Wiggenhauser PS., Kuhlmann C., Blum J., Giunta RE., Schenck T.
Laboratory Study, published in Acta Histochem (2020) — summary generated from the PubMed abstract.
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Laboratory Study
- Journal
- Acta Histochem (2020)
- Country
- Germany
- Reported sample size
- —
- Source database
- PubMed
- PMID
- 32197756
- DOI
- 10.1016/j.acthis.2020.151537
Abstract (original English)
The understanding of fat tissue plays an eminent role in plastic surgery as well as in metabolic research. Histopathological analysis of tissue samples provides insight in free fat graft survival and culture experiments help to better understand fat tissue derived stem cells (ASCs). To facilitate such experiments, modern image-based histology could provide an automatized approach to a large amount of data to gain not only qualitative but also quantitative data. This study was designed to critically evaluate image-based analysis of fat tissue samples in cell culture or in tissue probes and to identify critical parameters to avoid bias in further studies. In the first part of the study, ASCs were harvested and differentiated into adipocytes in cell culture. Histology was performed with the fluorescent dye BODIPY and the obtained digital images were analyzed using Image J software. In the second part of the study, digitalized histology of a previous in vivo study was subjected to automatized fat vacuole quantification using Image J. Both approaches were critically reviewed, and different software parameter settings were tested. Results showed that automatized digital image analysis allows the quantification of fat tissue probes with enough precision giving significant results. But the testing of different software parameters revealed a significant influence of parameters themselve
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
- • This is preclinical work; animal or laboratory results cannot be applied to humans.
Evidence level
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
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