mannlabs / py-lmd Goto Github PK

When executing code a deprecation warning comes up.

[/Users/sophia/mambaforge/envs/pylmd_new/lib/python3.9/site-packages/skimage/morphology/misc.py:39](): FutureWarning: `selem` is a deprecated argument name for `dilation`. It will be removed in version 1.0.Please use `footprint` instead.
  return func(image, footprint=footprint, *args, **kwargs)
[/Users/sophia/mambaforge/envs/pylmd_new/lib/python3.9/site-packages/skimage/morphology/misc.py:39](): FutureWarning: `selem` is a deprecated argument name for `binary_erosion`. It will be removed in version 1.0.Please use `footprint` instead.
  return func(image, footprint=footprint, *args, **kwargs)

This refers to the lines 967 and 968 in src/lib.py:

def tranform_to_map(self, 
                        dilation = 0,
                        erosion = 0,
                   debug = False):
...
self.offset_map = binary_dilation(self.offset_map , selem=disk(dilation))
self.offset_map = binary_erosion(self.offset_map , selem=disk(erosion))

Should fix this before scikit-image 1.0 is released.

Dear @sophiamaedler,

I am currently trying to collect cells from TMA cores, unfortunately, the core segmentation process (https://github.com/HMS-IDAC/UNetCoreograph) separates each core into a separate tif.

I could however calculate the pseduo coordenates of the ref points to the cells, by calculating distance to the crop coordenates. Question is, could I use reference points that are in a space outside my current .tif?

Ideas and feedback are welcome :)
Jose

As has been discussed offline it would be ideal to develop an optimisation approach to allow for the cutting of cells in close proximity while preserving membrane integrity. I have created this GitHub Issue to discuss in more detail with all interested parties (@fabsen-87, @josenimo, @LisaSchweizer) to ensure that we are implementing a tool that meets everyone's needs.

Based on the feedback I have received so far a first approach could be to selectively eliminate individual cells from densely segmented slide areas in such a way that we make sure that the membrane stays intact and we don't collect any "wrong" membrane areas and also don't lose membrane integrity. This would probably be implemented in an iterative fashion using proximity and area filters. We would of course lose some cells but would in return be able to collect all other cells without risk of contamination/destroying the sample.

One question that @GeorgWa and I had was at what point in the processing pipeline it would make the most sense from your perspective to implement such a filter:
(1) when loading the segmentation mask or
(2) when actually generating the cutting XML

In addition if you have any specific requirements that such a tool would need to fulfil it would be great if you could quickly outline them here.

Dear Sophia and George,
I am Jose, a PhD student at Fabian's lab in Berlin. I am very happy to hear your progress with the current py-lmd, its functionalities are great! For us however there is a key step missing. Obtaining the shape coordinates for each cell from the analysis pipeline output. I have uploaded a sample output table. I tried Googling a simple way to translate the cell's shape measurements into coordinates, but I was not able to find anything. Let me know if there is any way I can help.

Thank you again for the wonderful package, I look forward hearing from you.
Best,
Jose

MCMICRO17_output.csv

Dear @sophiamaedler @GeorgWa,

It has been some time, but I come to you with a specific question. I have a collaborator that want to collect shapes drawn on QuPath. I have created a jupyter notebook that tries to do this. However, I do not know if it is possible to label my polygons with CapIDs so that the lmd will automatically select for which wells to collect into.

I even created the list of dictionary entries for the segmentation loader when I noticed that I am not coming from a segmentation mask. Is there an easy way for me to add this well information to the xml file?

I am adding my notebook, and the test geoJSON file from Qupath.
https://filetransfer.mdc-berlin.de/?u=brPCXrXA&p=huzTHgBZ

Thank you for your help!
Best,
Jose