but in the case of my sparse data, the benefit of references to a "placeholder" image when one isn't present disappears - most likely i'll need to fill all the holes with copies of that image (could i compress only these "blank" images?). since i have only one Z, this is a 1 dimensional stack at this point, so that's fine. i can create per-channel tifs and use a companion.ome file, but still, each channel's tif has a header that CP apparently won't understand. Designed for biologists Load an example CellProfiler pipeline, a series of image-processing modules. So far i am trying to come up with a way to retain the tidiness of keeping images and metadata together (in Omero) while still allowing CP to load this correctly. this working was one of my giant assumptions. however this means replacing the OME-XML (which also has these counts) with this simpler header.ĭoes anyone have experience loading 4D data from an OME-TIFF into cellprofiler for processing? i hope i'm overlooking something obvious. by modifying the file's properties in FIJI and saving. Feedback so far from CP is that some pre-processing will be necessary that essentially creates a new header with the correct dimensional counts (t = 7, c = 3, etc), e.g. When CP loads that file, it derives 21 time points and 1 channel rather than 7 time points and 3 channels. this is true not only for my script-created, sparse OME-TIFF but also for the sample file here: Problem: to my knowledge, CellProfiler doesn't know how to read this information (at least the LoadImages and Metadata modules don't), even though i can import and view the stack successfully in Fiji and Omero. in these cases i instruct the reader (via UUID and IFD reference in the XML header) to either use a blank pane or the previous available image in that channel. Sparse means that there are some time points that do not have images for all channels (the reason: phototoxicity in long-running imaging of live cells). Materials: Images of C.I have created a single large OME-TIFF that describes sparse time lapse data (thanks to Roger Leigh's response to me on this forum some months ago). Modules can be combined to create a lesson plan appropriate for students ranging from high-school up to upper-level college biology students.Īctivity overview and description: Searching for new antibiotics using digital images of infected worms The exercise is written as a set of modules, such that the activities can be done up until any point. Images from an actual screen in which several compounds and extracts were found to rescue the worms from infection but had not previously been reported to have antimicrobial properties. elegans as a model organism for antibiotic research. In this exercise, you will have access to the following materials:īackground information on bacterial resistance, antibiotic discovery and C. elegans was used as an animal model to find small molecules that cure infection by theE.faecalis pathogen. This module exports measurements directly to a database or to a SQL-compatible format. The data is from a published study in which the nematode C. ExportToDatabase exports data directly to a database or in database readable format, including a CellProfiler Analyst properties file, if desired. This exercise will allow students to learn about how image analysis can be applied to screening chemicals for antibiotic drugs.
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