An image in this sense usually includes several individual monochromatic images that show the different wavelengths (channels) as well as images that show outlines of identified objects. Throughout CPA, the term image is meant to include all image data associated with an analyzed field-of-view. The directory structure does not matter as long as the file paths stored in the image table point to the correct images.
#Cellprofiler metadata how to#
Please visit Github wiki for instructions on how to install from source.These can be stored either locally or remotely and accessed via HTTP. This 3D functionality can be tested in advance of the official release by running CellProfiler from source. stacks of images with 3 dimensions X, Y, and Z. *Note: The upcoming release of CellProfiler 3.0 will add functionality to segment volumes, i.e. FIJI is thus a great open-source option to help visualize the segmentation of 3D image data when you need something more powerful than CellProfiler’s tiled view. The motion of scrolling through a Z stack can be reveal changes that might be otherwise be overlooked in a static slice.
#Cellprofiler metadata movie#
The orthoviews in FIJI provide an interactive way to evaluate a CellProfiler pipeline (see the movie below). An overlay will reveal exactly where the segmentation and source image agree or disagree. To prepare your image for FIJI, use the OverlayOutlines module in CellProfiler to overlay object outlines onto the original image remember to save the result using the SaveImages module. To activate the orthoview follow the menu tree: Image > Stacks > Orthogonal Views. We are also limited to a top-down or XY view of the 3D stack and might miss an important detail that would be clearly seen from a different perspective.įIJI is an open-source tool based on ImageJ2 that creates interactive orthoviews of a 3D stack to see the finer details of segmentation. Eventually, tiles can appear crowded and small, so if you’re interested in the segmentation of one object in particular or optimizing a threshold it can be difficult to see changes. However, the tiled slices of a volume start to become less effective as more tiles are displayed. Tiled slices are effective at identifying gross errors in the segmentation, such as widespread over- or under-segmentation. Segmentation can be validated by eye in much the same way as for 2D images: the tile pair that corresponds to the input image and the segmented image can be compared side-by-side. Tiling the slices of an image in this manner is a convenient form factor for displaying a large amount of information in a single view all at once we can see what is happening in the top, middle, and bottom of the volume. The output figure created by 3D capable modules in CellProfiler will show up to 9 slices of the volume image (see the figure below). If you would like to explore the example used in this post please check out the demo posted on the CellProfiler forum. This blog post contains advice on how to look at 3D images and the 3D segmentation image produced by CellProfiler and introduces orthoviews in FIJI. the monitor or phone you’re using to read this blog post!). However, the extra dimension in 3D complicates this approach, because the 3D image must be transformed in some manner in order to appear on a flat surface (e.g. Now that CellProfiler can handle 3D images, how do you view the results? When processing 2D images, the IdentifyPrimaryObjects module will produce a figure that displays the original image next to the segmented image to make validation fast and convenient, a side-by-side comparison.
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