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This module is used to assign a user-defined name to particular images or channels, and define their relationship to one another. Alternatively, resize the PlateTemplate.png image in Photoshop or in CellProfiler (using a pipeline consisting of the analysis modules Resize and SaveImages).ĭescribe the yeast plate images using the NamesAndTypes module. To do this, use Adobe Photoshop (or an alternative image modification program) to modify and save one of your images to use as a template, making the center of the plate pure white, and the surrounding background pure black. If your test plates are not the same size as those in the example images, you will need to create your own plate template. The template image will be used later in the pipeline to remove the edges and exterior region of the plastic plate, operating on the assumption that the plates are the same size from image to image. This is accomplished by using a single template image, “PlateTemplate.png”, to represent the image region corresponding to the interior of the plastic plate. ![]() This pipeline is flexible regarding the placement of each plate within the image, in that specific modules allow for CellProfiler to find the plate anywhere within the image, even if the position of the plate within the image varies from sample to sample. Example images and corresponding CellProfiler pipeline (see step 4)Īdjust the plate template image for your particular test images (if needed). This protocol was written for CellProfiler version 2.1.0. #Cellprofiler you must specify a pipeline filename to run softwareCellProfiler is optimized to take advantage of multiple computing processors on a single computer, but large image sets (greater than ~500 images) will likely require a computing cluster (see Alternate Protocol).ĭecompression software (e.g., WinZip, Stuffit) for unpacking compressed files, if not already included in your operating system.ĬellProfiler software (see step 1). The example image pipeline demonstrated here will be processed in ~1 minute per image on a single computer with a 2.67 GHz processor and 8 GB RAM. A complete list of compatible operating systems can be found at. CellProfiler is available for Macintosh, Windows, and Unix/Linux. A 64-bit operating system is strongly recommended. See Critical Parameters for more information about acquiring images and image file types.Ĭomputer with at least 4 GB of RAM and multiple processors each running at least 2 GHz. More than 100 file formats are currently readable by CellProfiler, including BMP, GIF, JPG, PNG, TIF, DIB, LSM, and FLEX. While this example only analyzes one image, it is possible to analyze hundreds of images on a single computer, or hundreds of thousands of images using a computing cluster (see Alternate Protocol). The images can be located within subfolders and need not be in a particular order or follow a particular naming convention. 2007) see Critical Parameters for guidance. Images can be taken with a flatbed scanner or digital camera ( Dahle et al. ![]() #Cellprofiler you must specify a pipeline filename to run PatchCellProfiler has been cited in more than a thousand papers and validated for a wide variety of biological applications, including yeast colony counting and classification, cell microarray annotation, yeast patch assays, cell-cycle classification, mouse tumor quantification, wound healing assays, and tissue topology measurement, as well as analysis of fluorescence microscopy images for measurement of cell size and morphology, cell cycle distributions, fluorescence staining levels, and other features of individual cells in images ( Lamprecht, Sabatini, and Carpenter 2007 Carpenter et al. The protocol uses the open-source, freely downloadable software package, CellProfiler. This unit outlines a protocol for the automated counting and analysis of yeast colonies growing on agar plates however, the methods described can be adapted to a wide variety of biological “objects” and can be used to measure a wide variety of features for each object. It is less tedious, more objective and quantitative, and, while the set up can be time-consuming, the analysis itself is usually much faster for large sample sets. Acquiring images and analyzing them automatically with image analysis software has several advantages over simple visual inspection. Many experiments in a biology laboratory involve visual inspection, such as examining yeast colonies or growth patches on agar plates, or examining live or stained cell samples by microscopy. ![]()
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