Merge pull request #48 from C-Achard/mwmw/docs-welcomeRST

minor doc edits

Author: C-Achard

docs/res/welcome.rst

@@ -1,11 +1,10 @@
 Introduction
 ===================
 
-Welcome to napari-cellseg3d !
---------------------------------------------
 
-Here you will find instructions on how to use the program.
-If the installation was successful, you'll see the napari-cellseg3d plugin
+Here you will find instructions on how to use the plug-in program.
+If the installation was successful, you'll see the napari-cellseg3D plugin
+
 in the Plugins section of napari.
 
 This plugin is intended for the review of labeled cell volumes [#]_ from mice whole-brain samples
@@ -15,9 +14,9 @@ any custom model written in Pytorch.
 From here you can access the guides on the several modules available for your tasks, such as :
 
 * Main modules :
-    * Review : :ref:`loader_module_guide`
-    * Inference: :ref:`inference_module_guide`
     * Training : :ref:`training_module_guide`
+    * Inference: :ref:`inference_module_guide`
+    * Review : :ref:`loader_module_guide`
 * Utilities :
     * Cropping (3D) : :ref:`cropping_module_guide`
     * Convert labels : :ref:`convert_module_guide`
@@ -32,9 +31,9 @@ Requirements
 .. important::
     A **CUDA-capable GPU** is not needed but **very strongly recommended**, especially for training.
 
-Requires manual installation of pytorch and some optional dependencies of MONAI.
+Requires installation of PyTorch and some optional dependencies of MONAI.
 
-* For Pytorch, please see `PyTorch's website`_ for installation instructions, with or without CUDA depending on your hardware.
+* For PyTorch, please see `PyTorch's website`_ for installation instructions, with or without CUDA depending on your hardware.
 
 * If you get errors from MONAI regarding missing readers, please see `MONAI's optional dependencies`_ page for instructions on getting the readers required by your images.
 
@@ -63,31 +62,35 @@ To use the plugin, please run:
 
 Then go into Plugins > napari-cellseg3d, and choose which tool to use.
 
-- **Review**: This module allows you to review your labels, from predictions or manual labeling, and correct them if needed. It then saves the status of each file in a csv, for easier monitoring.
-- **Infer**: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells.
+
 - **Train**:  This module allows you to train segmentation algorithms from labeled volumes.
+- **Infer**: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells.
+- **Review**: This module allows you to review your labels, from predictions or manual labeling, and correct them if needed. It then saves the status of each file in a csv, for easier monitoring.
 - **Utilities**: This module allows you to use several utilities, e.g. to crop your volumes and labels, compute prediction scores or convert labels
 
 
-Credits & acknowledgments
+Acknowledgments & References
 ---------------------------------------------
-This plugin has been developed by Cyril Achard and Maxime Vidal for the `Mathis Laboratory of Adaptive Motor Control`_.
+This plugin has been developed by Cyril Achard and Maxime Vidal for the `Mathis Laboratory of Adaptive Motor Control`_. 
+
+We also greatly thank Timokleia Kousi for her contributions to this project and the `Wyss Center`_ for project funding.
 
-The TRAILMAP models and original weights used here all originate from the `TRAILMAP project on GitHub`_ [1]_
+The TRAILMAP models and original weights used here all originate from the `TRAILMAP project on GitHub`_ [1]_.
 
 This plugin mainly uses the following libraries and software:
 
-* `Napari website`_
+* `napari website`_
 
-* `Pytorch website`_
+* `PyTorch website`_
 
 * `MONAI project website`_ (various models used here are credited `on their website`_)
 
 
 .. _Mathis Laboratory of adaptive motor control: http://www.mackenziemathislab.org/
+.. _Wyss Center: https://wysscenter.ch/
 .. _TRAILMAP project on GitHub: https://github.com/AlbertPun/TRAILMAP
-.. _Napari website: https://napari.org/
-.. _Pytorch website: https://pytorch.org/
+.. _napari website: https://napari.org/
+.. _PyTorch website: https://pytorch.org/
 .. _MONAI project website: https://monai.io/
 .. _on their website: https://docs.monai.io/en/stable/networks.html#nets