From ac3d3e7afbd7b8de466dc96aa01a5789cdbddd14 Mon Sep 17 00:00:00 2001 From: Niko Ehrenfeuchter <mail@he1ix.org> Date: Tue, 19 Apr 2022 13:50:43 +0200 Subject: [PATCH] Markdown formatting and conventions --- README.md | 69 +++++++++++++++++++++++++++++++++---------------------- 1 file changed, 42 insertions(+), 27 deletions(-) diff --git a/README.md b/README.md index 3674637..acde024 100644 --- a/README.md +++ b/README.md @@ -1,40 +1,55 @@ -# myosoft-imcf +# Myosoft-IMCF -imcf-adaptation of Myosoft, a Fiji script that identifies muscle fibers in images of sections +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 publication: <https://doi.org/10.1371/journal.pone.0229041> -original code: https://github.com/Hyojung-Choo/Myosoft/tree/Myosoft-hub +Original code: <https://github.com/Hyojung-Choo/Myosoft/tree/Myosoft-hub> +## `1_identify_fibers.py` -## 1_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 +- 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_identify_MHC_positive_fibers.py -- allows to manual re-run the MHC positive fiber detection. Useful in case you would like to re-run detection with a manual threshold for an image. +## `2a_identify_MHC_positive_fibers.py` -## 2b_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 +- Allows to manual re-run the MHC positive fiber detection. Useful in case you + would like to re-run detection with a manual threshold for an image. -## 2c_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 +## `2b_central_nuclei_counter.py` -## 3_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 +- 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. -All scripts store resulting ROI-zips, logs, Result tables and overview pngs. +## `2c_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. + +## `3_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 could look like this: -1. Run script 1) over night in batch mode on as many images as desired -2. you can potentially manually curate the resulting ROIs now, or directly move on to the next step -3. run either script 2b) or 2c), depending on the assay -4. with the results open, manually edit the ROIs and run script 3) for the final result +1. Run script 1) over night in batch mode on as many images as desired. +2. You can potentially manually curate the resulting ROIs now, or directly move + on to the next step. +3. Run either script 2b) or 2c), depending on the assay. +4. With the results open, manually edit the ROIs and run script 3) for the final + result. -- GitLab