M.X.Q.D. and A.K.S. contributed equally to this article.
High content analysis of differentiation and cell death in human adipocytes
Article first published online: 11 JUL 2013
Copyright © 2013 International Society for Advancement of Cytometry
Cytometry Part A
Volume 83, Issue 10, pages 933–943, October 2013
How to Cite
Doan-Xuan, Q. M., Sarvari, A. K., Fischer-Posovszky, P., Wabitsch, M., Balajthy, Z., Fesus, L. and Bacso, Z. (2013), High content analysis of differentiation and cell death in human adipocytes. Cytometry, 83: 933–943. doi: 10.1002/cyto.a.22333
- Issue published online: 20 SEP 2013
- Article first published online: 11 JUL 2013
- Manuscript Accepted: 10 JUN 2013
- Manuscript Revised: 5 JUN 2013
- Manuscript Received: 30 MAR 2013
- Hungarian Scientific Research Fund. Grant Numbers: OTKA NK 105046, OMFB-01626/2006
- Hungarian National Office for Research and Technology funds. Grant Numbers: GVOP-3.2.1–2004-04–0351/3.0, TECH09-A2–2009-0131
- European Social Fund. Grant Number: EU FP7 TRANSCOM IAPP 251506
- NKTH NTP Schizo08
- financed the. Grant Numbers: TÁMOP-4.2.2/B-10/1–2010-0024, TÁMOP-4.2.2.A-11/1/KONV-2012-0023
- “VÉD-ELEM” projects implemented through the New Hungary Development Plan
Additional Supporting Information may be found in the online version of this article.
|cytoa22333-sup-0002-suppfig1.tif||8176K||Figure S Comparison of cell recognition algorithms on a representative image. (a) Cell nuclei stained by Hoechst. (b) Adipocytes stained by Nile Red. (c) Preadipocytes stained by Nile Blue. (d-f) Object identification method presented in the article: nuclei were automatically identified as primary objects, contoured with green lines in (d). Secondary objects as adipocytes and preadipocytes were contoured with red lines in (e) and (f), respectively. (g) A merged image of a, b and c. Arrows show typical preadipocytes, asterisks indicate adipocytes. (h) The result of the segmentation method developed by McDonough (7,8). (i) Ground-truth segmentation by human manual contouring. Table summarizes identified objects enumerated from the two automated methods and manual segmentation. Area covered by identified objects is calculated as percentage of the total image. Accuracy of each algorithm is evaluated as the proportion of the true prediction achieved by the segmentation. F-measure, which is a more widely used statistical parameter to evaluate imaging algorithms, was also calculated (26). Both parameters ranked our method higher.|
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