Segmenting time-lapse phase contrast images of adjacent NIH 3T3 cells.

TitleSegmenting time-lapse phase contrast images of adjacent NIH 3T3 cells.
Publication TypeJournal Articles
Year of Publication2013
AuthorsChalfoun J, Kociolek M, Dima A, Halter M, Cardone A, Peskin A, Bajcsy P, Brady M
JournalJournal of microscopy
Date Published2013 Jan
KeywordsAnimals, Cell Adhesion, Cell Count, Cell Division, Cell Shape, Computational Biology, Fibroblasts, Image Processing, Computer-Assisted, Mice, Microscopy, Phase-Contrast, NIH 3T3 Cells, Reproducibility of results, Sensitivity and Specificity, Time-Lapse Imaging

We present a new method for segmenting phase contrast images of NIH 3T3 fibroblast cells that is accurate even when cells are physically in contact with each other. The problem of segmentation, when cells are in contact, poses a challenge to the accurate automation of cell counting, tracking and lineage modelling in cell biology. The segmentation method presented in this paper consists of (1) background reconstruction to obtain noise-free foreground pixels and (2) incorporation of biological insight about dividing and nondividing cells into the segmentation process to achieve reliable separation of foreground pixels defined as pixels associated with individual cells. The segmentation results for a time-lapse image stack were compared against 238 manually segmented images (8219 cells) provided by experts, which we consider as reference data. We chose two metrics to measure the accuracy of segmentation: the 'Adjusted Rand Index' which compares similarities at a pixel level between masks resulting from manual and automated segmentation, and the 'Number of Cells per Field' (NCF) which compares the number of cells identified in the field by manual versus automated analysis. Our results show that the automated segmentation compared to manual segmentation has an average adjusted rand index of 0.96 (1 being a perfect match), with a standard deviation of 0.03, and an average difference of the two numbers of cells per field equal to 5.39% with a standard deviation of 4.6%.

Alternate JournalJ Microsc
PubMed ID23126432