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Additional Supporting Information may be found in the online version of this article.

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cytoa22333-sup-0001-suppinfo.doc41KSupporting Information.
cytoa22333-sup-0002-suppfig1.tif8176KFigure 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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