Dear all,
I’m at the moment testing whether both CP versions give the same results on my images. Unfortunately they don’t, and I don’t understand why. If I run my standard pipeline on v11710, then convert it to a CP 2.1 pipeline and let it run again on the very same images, I get a discrepancy in the nuclei counts. The difference between versions v11710 and 2.1 are maybe around +/- 2 nuclei per image and not systematic. I admit that this is fairly low, but it introduces an error of around 1% in the end result I calculate from the CP results (Percentage of cells positive for a marker):
I don’t see any differences in the IdentifyPrimaryObjects modules, and the Illumination correction I do beforehand is also the same. Have there been any changes in the algorithms which could explain this?
Thanks a lot for your help!
Here’s the Identify Primary Objects part from v11710:
IdentifyPrimaryObjects:[module_num:14|svn_version:\'10826\'|variable_revision_number:8|show_window:False|notes:\x5B\'Identify the nuclei from the nuclear stain image. Some manual adjustment of the smoothing filter size and maxima supression distance is required to optimize segmentation.\'\x5D]
Select the input image:CorrBlue
Name the primary objects to be identified:Nuclei
Typical diameter of objects, in pixel units (Min,Max):6,30
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 the thresholding method:Otsu Adaptive
Threshold correction factor:1
Lower and upper bounds on threshold:0.008,1.0
Approximate fraction of image covered by objects?:0.1
Method to distinguish clumped objects:Intensity
Method to draw dividing lines between clumped objects:Intensity
Size of smoothing filter:10
Suppress local maxima that are closer than this minimum allowed distance:5
Speed up by using lower-resolution image to find local maxima?:Yes
Name the outline image:NucOutlines
Fill holes in identified objects?:Yes
Automatically calculate size of smoothing filter?:Yes
Automatically calculate minimum allowed distance between local maxima?:Yes
Manual threshold:0.0
Select binary image:MoG Global
Retain outlines of the identified objects?:Yes
Automatically calculate the threshold using the Otsu method?:Yes
Enter Laplacian of Gaussian threshold:.5
Two-class or three-class thresholding?:Two classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
Enter LoG filter diameter:5
Handling of objects if excessive number of objects identified:Continue
Maximum number of objects:500
Select the measurement to threshold with:None
And here from 2.1:
IdentifyPrimaryObjects:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify the nuclei from the nuclear stain image. Some manual adjustment of the smoothing filter size and maxima supression distance is required to optimize segmentation.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True]
Select the input image:CorrBlue
Name the primary objects to be identified:Nuclei
Typical diameter of objects, in pixel units (Min,Max):6,30
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
Method to distinguish clumped objects:Intensity
Method to draw dividing lines between clumped objects:Intensity
Size of smoothing filter:10
Suppress local maxima that are closer than this minimum allowed distance:5
Speed up by using lower-resolution image to find local maxima?:Yes
Name the outline image:NucOutlines
Fill holes in identified objects?:Yes
Automatically calculate size of smoothing filter for declumping?:Yes
Automatically calculate minimum allowed distance between local maxima?:Yes
Retain outlines of the identified objects?:Yes
Automatically calculate the threshold using the Otsu method?:Yes
Enter Laplacian of Gaussian threshold:.5
Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
Enter LoG filter diameter:5
Handling of objects if excessive number of objects identified:Continue
Maximum number of objects:500
Threshold setting version:1
Threshold strategy:Adaptive
Thresholding method:Otsu
Select the smoothing method for thresholding:Automatic
Threshold smoothing scale:1
Threshold correction factor:1
Lower and upper bounds on threshold:0.008,1.0
Approximate fraction of image covered by objects?:0.1
Manual threshold:0.0
Select the measurement to threshold with:None
Select binary image:MoG Global
Masking objects:From image
Two-class or three-class thresholding?:Two classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
Method to calculate adaptive window size:Image size
Size of adaptive window:10