Pipeline 1 : CellProfiler tutorials

For this tutorial, we will be building a pipeline
to find individual worms in subregions and then extract measurements. To begin, start CellProfiler 2.1. The CellProfiler
interface consists of the pipeline panel to the left, which is divided into input modules
and analysis modules. A space for module notes for annotations and a file list will appear
to the right. To provide CellProfiler with images files to be analyzed, drag and drop
the input image files into the file list space. The input module metadata enables extraction
of metadata information from the image file names and folders. Click on metadata and a
list of input modules and the modules settings for the Metadata module will appear on the
right. Check the box Extract Metadata and a number
of options will appear underneath. Select Extract from File/folder name as the extraction
method in order to extract the well ids from the file names using regular expressions. To learn more about regular expressions, click
on the question mark next to this option in the module settings. Click Add another
extraction method and then extract the plate and gene names from the folder names
using regular expressions. Verify your metadata extraction using the
update button below the module settings to ensure that each input image from the images
module is associated with the plate, gene, and well id metadata. Next, select the input module names and types.
Set the input image type to Color Images and assign the images the name RawData. The group’s modules is not needed for this
assay, so it can be left as its default setting. In order to load the worm model, you will
need to adjust the output settings. Click on the View Output settings button in the
button left of the CellProfiler interface and then adjust the default output folder
to point to the folder where you want to store your output. Once the input modules are all set, the analysis
pipeline is built up by inserting modules into the analysis panel. Modules are added
by clicking the plus sign next to Adjust Modules in the bottom left corner of the interface
to bring up a list of selectable modules. Most object detection methods in CellPrpofiler
are designed for bright objects on a dark background. Since the input images are color
images with a bright background, the first thing we need to do is invert the image pixel
intensities. Adding the module called ImageMath located under the module category Imaging
Processing. Add the module by selecting and double clicking
it. The module’s now been added to your analysis pipeline. When the module is selected,
its settings are shown on the panel to right where they can be adjusted. For this module,
select invert as the operation called the output image InvertedRaw and select RawData
as the input. Most images processing algorithms operate
on greyscale data, so the next thing we need to do is convert the inverted color image
into a greyscale image in two different ways. First, we add the module color to grey, and
then combine the red, green, and blue channels of the inverted raw image into a single image
set that we will call a OrigGrey. The image will be used to identify the worms. To see the effect of a module, click on start
test mode. It is now possible to step through the analysis pipeline and model the output
at each step. Add another color to grey module and this time, split the red, green, and blue
channels and called them OrigRed, OrigGreen, and OrigBlue. These images will be used for
identification of bubbles and fatty regions. As it turns out, bubbles have high contrast
in the red image channel and appear brighter than the worms in the inverted image. The
same is true for the edges of the wells visible in the corners of the image. We can detect
both of these structures in the origRed image using the object identification module IdentifyPrimaryObjects.
using a manual intensity threshold of .5, and allowing a size range from 10 pixels to
10,000 pixels. Make sure not to discard objects touching
the border of the image, as we do not want to lose clusters of worms were only one or
a few worms are partly outside the field of view. In this case, we will not attempt to
separate touching objects, and we will call the output Well_n_Bubbles. We also want to remove shadows surrounding
bubbles and well edges. to do this, we will add the module ExpandorShrinkObjects and select
expand objects by specified number of pixels, and expend the Well_n_bubbles object by 5
pixels. We will call the output expanded Wells_n_bubbles. Next, we use the mask image module to mask
the origGray image with expanded_well_n_bubbles, to get a new image, MaskedGray, without artifacts.
Make sure to invert the mass in order to keep worms and remove artifacts. We will now use
IdentifyPrimaryObjects to separate worms and worm clusters from the image background. With the MaskedGrey image as input, we set
the size range from between 20 pixels and 30000 pixels in order to remove small debris
and ensure that large worm clusters are kept. We use automated thresholding with global
robust background as the method. Since there’s a risk for empty wells, we also set a minimum
threshold level at 0.09. In this case, we will not attempt to separate touching objects.
Uncheck the fill holes and identify objects setting. Finally, we will give the name WormObjects
to the Worm Objects. We can view the worm identification results
by stepping through this module in test mode. Add the ConvertObjectsToImageModule to convert
the worm objects to a binary image, call the output image WormBinary. Add the module UntangleWorms from the Worm
Toolbox. Set it to Untangle, using the WormBinary as input and choose both as the overlapping
style. This sets the module to work on overlapping mass when measuring size and shape, but non
overlapping mass when measuring intensity. Click the box for retaining both kinds of
outlines and set the training set file name to the DefaultWormModel.xml file. Again, we can view the untangling results
by stepping through the module in test mode. As check the results use overlay outlines
to overlay the worm outlines on the original RawData image. Select the two outlines and
call the output image OrigOverlay. Now the worms have been identified in untangling,
we want to begin to extract shape and intensity measurements. We start by adding MeasureObjectSizeShape
to the pipeline and measure from the NonOverlappingWorm objects. Be sure to deselect the Calculate
the Zernike features since these measurements are not relevant to this context and add computational
time. Since the true width of the worms will be
biased by the width of the worm model, we want to extract the actual average width from
the WormObjects. We achieve this by using the Morph module in order to calculate the
distance transform of the WormBinary image. Select distance as the operation to perform
and call the output image WormWidthsFromBinary. Add the measure ObjectIntensity module to
measure worm intensities. Select OrigRed OrigBlue and WormsWidthFromBinary as the images to
measure. Select NonOverlappingWorms as the objects to measure. The measurements on WormWidthsFromBinary
will provide information on the worm widths while measurements on OrigRed and OrigBlue
will provide information on the stained distribution and color. The StraightenWorms module from the Worm Toolbox
extracts intensity measurements from subregions of the worms after digitally straightening
the worms and dividing them into a fixed number of transfer segments in longitudinal stripes.
These measurements can provide information on where fat is located inside the worm. In this module, we select NonOverlappingWormsToStraightren
and specify to defaultwormmodel.xml as a training set. We then select a single transfer segment
and five longitudinal stripes. Note that if a head marker is available, all worms could
be automatically aligned. We select OrigBlue as the image for measurements. Before identifying the fat subregions of the
worm, we want to mask away all regions in the image that should not be considered as
a worm. We do this by adding the module MaskImage. Set OrigBlue as the input image and WormsBinary
as the masking image. The OrigBlue image is selected to define fatty regions. Since the
has its greatest contrast in the blue image channel. The output of this module is called
MaskedBlue. Now we use identify primary objects to find the fatty regions. This time, we let the diameter vary in size
from 5 pixels to 10,000 pixels and specify a fixed intensity threshold for whats regarded
as fat, set for .4 for this dataset. This fixed threshold was selected by trying a range
of thresholds and viewing the result and should be adjusted for a new dataset. We do not attempt
to separate clumped objects burt we do retain the outlines. We will call the output objects FatObjects
and the outlines FatOutlines. To be able to see the detected fat outlines
we will use the OverlayOutlines module to add them to the image to which we already
outlined the worms, namely OrigOverlay. We do this by adding the module OverlayOutlines
and selecting OrigOverlay as the image on which to display the outlines, selecting FatOutlines
for the outlines and calling the output image OrigOverlayWithFat. Run the pipeline in test mode to individually
confirm the output. Now extract the shape in intensity measurements
from the FatObjects by adding a module MeasureObjectSizeShape. Uncheck the Run Zernike feature again. We also add the module MeasureObjectIntensity
and measure intensity features from OrigBlue and OrigRed images with FatObejcts as the
selected Objects. In order to assign each detected fatty region
to a worm we add the module RelateObjects with FatObjects as the children and NonOverlappingWorms
as the parents. We also check the calculate per-parent means for all children measurements
box to aggregate the fast statistics for each worm. Finally, we’ll save all measurements segmentations,
worm outlines, as well as the images with the overlayed outlines. At the module SaveImages
and save the OrigOverlay with FatImage using the RawData file name as the prefix and _res
as the appended suffix Next, add another SaveImages module to save
the worm outlines as they will be used for later in the machine learning step in CellProfilerAnalyst.
Select the overlap Worm Outlines as the input, the RawData name for the prefixes above and
_outlines as the suffix. Also, check the record file and path information to the saved image
box. Add a third SaveImages module to save the
segmentation mass called OverlappingWorms and save them as objects with the file name
extension _worm_objects and the format as tif which is the only format for saving overlapping
objects. These will be used for input for pipeline number four. Export all measurements to an SQLite database
using the ExportToDatabaseModule. Choose SQLite as the database type and name the experiment
MyExperiment. Check the box to create a CellProfiler Analyst properties file and choose NonOverlappingWorms
as the objects used for location. The plate type should be 96. Set the plate metadata to plate and a well
metadata to well and specify that only selected object will be used for export and select
the NonOverlappingWormObjects. Your pipeline is now complete, so exit test
mode by pressing the exit tested button. Since we’ve finished previewing the results, the
display windows can be optionally closed to save time and memory during the analysis run.
Select hide all windows on run from the window menu item to close all the display windows
and have them remain closed during the analysis run. Then, press the Analyze Images button
to run the pipeline on all images. The output will be in an SQLite database with
measurements, a properties file for data exploration in CellProfiler Analyst, a set of images showing
outlines of worms in fatty regions on top of the raw data, a set of images without lines
only, and a set of segmentation masks describing the overlapping worms.

Leave a Reply

Your email address will not be published. Required fields are marked *