quantification:mcquant error after segmentation with Mesmer

[nancyliy]

I encountered the following error when running the MCMICRO pipeline at the quantification stage of the workflow:

Error executing process > 'quantification:mcquant (1)'

Caused by:
Process quantification:mcquant (1) terminated with an error exit status (1)

Command executed:

python /app/CommandSingleCellExtraction.py --image pilot_2--mesmer.ome.tif --masks cell*.tif --output . --channel_names markers.csv

Command exit status:
1

Command output:
{'masks': ['cell.tif'], 'image': 'pilot_2--mesmer.ome.tif', 'channel_names': 'markers.csv', 'output': '.', 'intensity_props': {'intensity_mean'}, 'mask_props': None}
Extracting single-cell data for pilot_2--mesmer.ome.tif...

Command error:
WARNING: DEPRECATED USAGE: Environment variable SINGULARITYENV_TMPDIR will not be supported in the future, use APPTAINERENV_TMPDIR instead
INFO: underlay of /usr/bin/nvidia-smi required more than 50 (434) bind mounts
Traceback (most recent call last):
{'masks': ['cell.tif'], 'image': 'pilot_2--mesmer.ome.tif', 'channel_names': 'markers.csv', 'output': '.', 'intensity_props': {'intensity_mean'}, 'mask_props': None}
Extracting single-cell data for pilot_2--mesmer.ome.tif...
File "/app/CommandSingleCellExtraction.py", line 11, in
SingleCellDataExtraction.MultiExtractSingleCells(**args)
File "/app/SingleCellDataExtraction.py", line 263, in MultiExtractSingleCells
ExtractSingleCells(masks,image,channel_names,output, mask_props=mask_props, intensity_props=intensity_props)
File "/app/SingleCellDataExtraction.py", line 236, in ExtractSingleCells
scdata_z = MaskZstack(masks_loaded,image,channel_names_loaded_checked, mask_props=mask_props, intensity_props=intensity_props)
File "/app/SingleCellDataExtraction.py", line 145, in MaskZstack
MaskChannel(masks_loaded[mask_names[nm]],image_loaded_z, intensity_props=intensity_props)
File "/app/SingleCellDataExtraction.py", line 35, in MaskChannel
dat = measure.regionprops_table(
File "/home/users/nancyliy/.local/lib/python3.9/site-packages/skimage/measure/_regionprops.py", line 974, in regionprops_table
regions = regionprops(label_image, intensity_image=intensity_image,
File "/home/users/nancyliy/.local/lib/python3.9/site-packages/skimage/measure/_regionprops.py", line 1290, in regionprops
props = RegionProperties(sl, label, label_image, intensity_image,
File "/home/users/nancyliy/.local/lib/python3.9/site-packages/skimage/measure/_regionprops.py", line 288, in init
raise ValueError('Label and intensity image shapes must match,'
ValueError: Label and intensity image shapes must match, except for channel (last) axis.

The input registration image pilot_2--mesmer.ome.tif and the output mask cell*.tif are too large to be shared here but I am happy to share those files via OneDrive if needed to examine the image shapes.

Thank you!

Nextflow_Pilot2.txt

[ArtemSokolov]

Hi @nancyliy,

The following error indicates that the input image and the label mask have different dimensions:

ValueError: Label and intensity image shapes must match, except for channel (last) axis.

What is the width & height of pilot_2--mesmer.ome.tif and cell.tif? Was there any additional processing done to the images? It would be strange for Mesmer to produce a mask image that has different dimensions than the original image.

[nancyliy]

@ArtemSokolov Thanks for your suggestion. I would like to know how to get the dimensions of input pilot_2--mesmer.ome.tif and the mask cell.tif printed from within the SingleCellDataExtraction.py attached here as sanity checks.

run_quantifictation.txt
CommandSingleCellExtraction.txt
SingleCellDataExtraction.txt

There was no additional processing done to the images beyond MCMICRO pipeline.

Thank you!

[ArtemSokolov]

There are a lot of different ways to determine image dimensions, depending on what's installed on your system.

• Most image viewers, like ImageJ, napari, etc., will display the information when you load the image.
• You can install and use showinf from bioformats tools (https://docs.openmicroscopy.org/bio-formats/latest/users/comlinetools/display.html) to display basic image information
• You can also pip install tifffile and query the dimensions programmatically with

import tifffile
tifffile.imread('exemplar-001/registration/exemplar-001.ome.tif').shape
# (12, 3139, 2511)
tifffile.imread('exemplar-001/segmentation/mesmer/exemplar-001.ome.tif').shape
# (3139, 2511)

In the above example, we can see that the input image contains 12 channels, each channel being a 3139 x 2511 pixel image. Similarly, the segmentation mask is a single-channel 3139 x 2511 pixel image.

[nancyliy]

Thanks @ArtemSokolov ! I used your advice I found out my mask has a dimension of (11520, 19392, 2) -- suggesting it has 2 compartments. Then instead of using --compartment both, I run the mcmicro pipeline on nuclear and whole-cell separately and that successfully overcame the problem:) A big big thank you to your timely help!