diff --git a/README.md b/README.md
index 81a4b80af5f90a525212f294b2eb855e6be3a186..5b106e1f781e1988516ad1adad9ca4a199793ee0 100644
--- a/README.md
+++ b/README.md
@@ -9,22 +9,22 @@ Original publication: <https://doi.org/10.1371/journal.pone.0229041>
 
 Original code: <https://github.com/Hyojung-Choo/Myosoft/tree/Myosoft-hub>
 
-## `1_identify_fibers.py`
+## [`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
+- Will identify all fibers based on the membrane staining using [Cellpose](https://github.com/MouseLand/cellpose) segmentation, 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
+  - Need to be installed ont the machine where the script is run. Follow [this guide](https://wiki.biozentrum.unibas.ch/display/IMCF/Cellpose+python+environment) to create the environment.
+- Will now also save the Cellpose 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`
+## [`2a_identify_MHC_positive_fibers.py`](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.
 
-## `2b_central_nuclei_counter.py`
+## [`2b_central_nuclei_counter.py`](2b_central_nuclei_counter.py)
 
 - Will identify centralized nuclei given a ROI-zip together with its
   corresponding image.
@@ -32,14 +32,14 @@ Original code: <https://github.com/Hyojung-Choo/Myosoft/tree/Myosoft-hub>
   information of a MHC staining channel.
 - The ROI color code is annotated in the results table.
 
-## `2c_fibertyping.py`
+## [`2c_fibertyping.py`](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`
+## [`3_manual_rerun.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.