diff --git a/README.md b/README.md
index 490a859e44d6c9088113b064767f679b96282603..f15a76d72b20e3b0a5984e324a948b6e5557f936 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 **Structure tensor tutorials** for the implementation in https://github.com/Skielex/structure-tensor. This implementation is compatible with the python package `cupy`. Therefore, if you have large volumes to process and a graphical processing unit (GPU), you can speed up your computations simply by having a working installation of [cupy](https://docs.cupy.dev/en/stable/install.html).
 
-The tutorials demonstrate the use of the structure tensor tool for the analysis of 2D and 3D data. The tutorials come with a set of utils (helper functions) to inspect 2D and 3D data, and the results from the structure tensor. The 2D and 3D examples come as python scripts (.py) and Jupyter Notebooks (.ipynb), the latter is more complete and pedagogical, as it comes with explanations. With these tutorials we would like you to 1) learn how to choose the parameters to obtain desirable results, 2) see different options for visualising the output of the structure tensor, and 3) get inspiration for scientific questions that you could answer with the structure tensor tool.
+The tutorials demonstrate the use of the structure tensor tool for the analysis of 2D and 3D data. The tutorials come with a set of utils (helper functions) to inspect 2D and 3D data, and analyse the structure tensor output. The 2D and 3D examples come as python scripts (.py) and Jupyter Notebooks (.ipynb), the latter is more complete and pedagogical, as it comes with explanations. With these tutorials we would like you to 1) learn how to choose the parameters to obtain desirable results, 2) see different options for visualising the output of the structure tensor, and 3) get inspiration for scientific questions that you could answer with the structure tensor tool.
 
 **To dos:**