![]() ![]() These properties make the ciliated protozoan an appropriate organism to determine the health of an aquatic environment. Additionally, they are able to consume free organic material from the environment if necessary. They play an integral part in the community by connecting the food chain between bacteria and small phytoplankton to larger metazoa and zooplankton. The optimized methods reported in this paper provide scientists with a convenient tool to perform cell counting for Tetrahymena ecotoxicity assessment.Ĭiliated protozoa are unicellular eukaryotes commonly found in aquatic environments. Among the five methods tested, all of them could yield decent results, but the deep-learning-based SDM displayed the best performance for Tetrahymena cell counting. We conducted Tetrahymena number measurement using five different methods: particle analyzer method (PAM), find maxima method (FMM), trainable WEKA segmentation method (TWS), watershed segmentation method (WSM) and StarDist method (SDM), and compared their results with the data obtained from the manual counting. In this study, we established the ImageJ-based workflow to quantify ciliate numbers in a high-throughput manner. Although advanced counting devices are available, the specialized and usually expensive machinery precludes their prevalent utilization in the regular laboratory routine. You might also want to try the ‘Convoluted Background Subtraction’ tool available in the BioVoxxel Toolbox, which appears to do a better job at suppressing/removing the halos surrounding your stained cells, which potentially helps with the subsequent thresholding and particle selection.Previous methods to measure protozoan numbers mostly rely on manual counting, which suffers from high variation and poor efficiency. Or you could smooth the particles on the thresholded image using binary filters to remove irregularities in the particles shape that cause the watershed algorithm to split single particles (I would expect single nuclei to be pretty smooth, round objects). So, the watershed step might not be required for your analysis.Īlternatively, you could try to use the ‘Irregular Watershed’ tool available in the Biovoxxel Toolbox ( BioVoxxel Toolbox). Looking at your sample image, the nuclei appear to be quite sparse with hardly any nuclei directly touching each other. When you say that you ‘can see lines within the cells’ that is due to the watershed and occurs if the structure has an irregular shape that is split by the watershed operation, which tries to separate touching particles. Screenshot of detected particles shown on original image: Run("Analyze Particles.", "size=5-150 circularity=0.65-1.00 display clear add") Volko run("Subtract Background.", "rolling=25") Either way, I don’t think that you will get any meaningful cell count from that clump and it is probably best excluded from the analysis. I assume that this is either some dirt or a larger clump of cells. The difficulty is the clump of material in the bottom right corner of the image. The code below hopefully gets you a bit closer to what you are aiming for. When you do the watershed, the options in the ‘Binary Options’ are also set such that the watershed is carried out on the black background rather than the white particles that you thresholded.įinally, your image doesn’t appear to be calibrated (scale set to 1 pixel = 1 inch). At the moment, you are selecting the halo around the centres of the stained structures rather than their centre - not sure whether this is your intention. How did you stain and image these nuclei?Īnyway, to threshold the bright spots that form the centre of your particles (I assume that this is what you try to count), I would suggest not to select the ‘Light Background’ option when you do the background subtraction. The stained particles also appear to have some rather strange halos more like a phase contrast image than a fluorescent image. Looking at your image, it looks a bit unusual for fluorescently labeled nuclei - I would expect the contrast of the nuclei relativeto the background to be considerably higher for most of the conventional fluorescent nuclei stains (e.g. ![]()
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