myosoft-imcf
imcf-adaptation of Myosoft, a Fiji script that identifies muscle fibers in images of sections
original publication: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0229041
original code: https://github.com/Hyojung-Choo/Myosoft/tree/Myosoft-hub
- myosoft-imcf_identify_fibers.py
- Will identify all fibers based on the membrane staining using WEKA pixel classification, filter them according to the morphometric gates and save the corresponding ROIs
- will now also save the WEKA segmentation as a binary so it can be edited manually. If you do so, you need to run the "extended particle analyzer" manually as well to choose & apply the morphometric gates.
- can be run in batch
2a) myosoft-imcf_central_nuclei_counter.py
- will identify centralized nuclei given a ROI-zip together with its corresponding image
- identification is based on the same logic as before incorporating the information of a MHC staining channel
- the ROI color code is annotated in the results table
2b) myosoft-imcf_fibertyping.py
- identifies positive fibers in up to 3 channels given a ROI-zip together with its corresponding image
- includes identification of double and triple positive combinations
- the ROI color code is annotated in the results table
- myosoft-imcf_manual_rerun.py
- requires an already open image with an already populated ROI manager
- allows to manually select measurement parameters and the measurement channel
- extracts the ROI color code and stores it in the result table
All scripts store resulting ROI-zips, logs, Result tables and overview pngs.
A potential workflow would look like this:
- Run script 1) over night in batch mode on as many images as desired
- you can potentially manually curate the resulting ROIs now, or directly move on to the next step
- run either script 2a) or 2b), depending on the assay
- with the results open, manually edit the ROIs and run script 3) for the final result