Hi CellProfiler community,
I’m in the process of developing a pipeline for the automatic identification and measurement of fission yeast cells, based on a cytoplasmic fluorescent protein and DIC image subtraction. The pipeline keeps wanting to split cells into two prematurely, and I was wondering if you guys have any ideas how I could fix this. (Example picture is below).
My basic pipeline is this:
LoadImages InvertIntensity Multiply IdentifyPrimAutomatic MeasureObjectAreaShape OverlayOutlines SaveImages
Which corresponds to loading a GFP and DIC image, inverting DIC image, and multiplying GFP and inverted DIC to get crisper edges around the fluorescent image (which aids in the segmentation of clumped cells). I then use IdentifyPrimAutomatic module, the code for which I’ve pasted at the end of this post.
Any ideas? I’m wondering if IdentifyPrimAutomatic is biased for round objects, and the elongated fission yeast cells are throwing it for a loop. Another idea I’ve considered is using a nuclear marker and then using IdentifySecondaryObjects, which will prevent pre-mature splitting of the cell if there is only one nuclear (primary) object.
Thanks in advance for any input.
Module #6: IdentifyPrimAutomatic revision - 12
What did you call the images you want to process? Multiplied
What do you want to call the objects identified by this module? Cells
Typical diameter of objects, in pixel units (Min,Max): 45,100
Discard objects outside the diameter range? Yes
Try to merge too small objects with nearby larger objects? No
Discard objects touching the border of the image? Yes
Select an automatic thresholding method or enter an absolute threshold in the range [0,1]. To choose a binary image, select “Other” and type its name. Choosing ‘All’ will use the Otsu Global method to calculate a single threshold for the entire image group. The other methods calculate a threshold for each image individually. “Set interactively” will allow you to manually adjust the threshold during the first cycle to determine what will work well. Set interactively
Threshold correction factor 1
Lower and upper bounds on threshold, in the range [0,1] 0,1
For MoG thresholding, what is the approximate fraction of image covered by objects? 0.01
Method to distinguish clumped objects (see help for details): Intensity
Method to draw dividing lines between clumped objects (see help for details): Intensity
Size of smoothing filter, in pixel units (if you are distinguishing between clumped objects). Enter 0 for low resolution images with small objects (~< 5 pixel diameter) to prevent any image smoothing. Automatic
Suppress local maxima within this distance, (a positive integer, in pixel units) (if you are distinguishing between clumped objects) Automatic
Speed up by using lower-resolution image to find local maxima? (if you are distinguishing between clumped objects) Yes
Enter the following information, separated by commas, if you would like to use the Laplacian of Gaussian method for identifying objects instead of using the above settings: Size of neighborhood(height,width),Sigma,Minimum Area,Size for Wiener Filter(height,width),Threshold Do not use
What do you want to call the outlines of the identified objects (optional)? CellOutlines
Do you want to fill holes in identified objects? Yes
Do you want to run in test mode where each method for distinguishing clumped objects is compared? No