minor doc updates

Author: MMathisLab

docs/res/welcome.rst

@@ -2,13 +2,13 @@ Introduction
 ===================
 
 
-Here you will find instructions on how to use the plug-in program.
+Here you will find instructions on how to use the plugin for direct-to-3D segmentation.
 If the installation was successful, you'll see the napari-cellseg3D plugin
 in the Plugins section of napari.
 
 This plugin was initially developed for the review of labeled cell volumes [#]_ from mice whole-brain samples
 imaged by mesoSPIM microscopy [#]_ , and for training and using segmentation models from the MONAI project [#]_,
-or any custom model written in Pytorch.
+or any custom model written in PyTorch.
 It should be adaptable to other tasks related to detection of 3D objects, as long as labels are available.
 
 
@@ -26,32 +26,34 @@ From this page you can access the guides on the several modules available for yo
     * Defining custom models directly in the plugin (WIP) : :ref:`custom_model_guide`
 
 
-Requirements
+Installation
 --------------------------------------------
 
-.. important::
-    A **CUDA-capable GPU** is not needed but **very strongly recommended**, especially for training.
-
-Requires installation of PyTorch and some optional dependencies of MONAI.
+You can install `napari-cellseg3d` via [pip]:
 
-* For PyTorch, please see `PyTorch's website`_ for installation instructions, with or without CUDA depending on your hardware.
+  ``pip install napari-cellseg3d``
 
-* 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.
+  For local installation, please run:
 
-.. _MONAI's optional dependencies: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies
-.. _PyTorch's website: https://pytorch.org/get-started/locally/
+  ``pip install -e .``
 
-Installation
+Requirements
 --------------------------------------------
 
-You can install `napari-cellseg3d` via [pip]:
+.. important::
+    A **CUDA-capable GPU** is not needed but **very strongly recommended**, especially for training.
 
-    ``pip install napari-cellseg3d``
+This package requires you have napari installed first.
 
-For local installation, please run:
+It also depends on PyTorch and some optional dependencies of MONAI. These come in the pip package above, but if
+you need further assistance see below.
 
-    ``pip install -e .``
+* For help with 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.
+
+.. _MONAI's optional dependencies: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies
+.. _PyTorch's website: https://pytorch.org/get-started/locally/
 
 
 Usage
@@ -61,7 +63,7 @@ To use the plugin, please run:
 
     ``napari``
 
-Then go into Plugins > napari-cellseg3d, and choose which tool to use.
+Then go into Plugins > napari-cellseg3d, and choose which tool to use:
 
 
 - **Train**:  This module allows you to train segmentation algorithms from labeled volumes.
@@ -73,11 +75,11 @@ See above for links to detailed guides regarding the usage of the modules.
 
 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, supervised by Mackenzie Mathis 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 were ported to PyTorch but originate from the `TRAILMAP project on GitHub`_ [1]_.
 
 This plugin mainly uses the following libraries and software:
 
@@ -88,7 +90,7 @@ This plugin mainly uses the following libraries and software:
 * `MONAI project website`_ (various models used here are credited `on their website`_)
 
 
-.. _Mathis Laboratory of adaptive motor control: http://www.mackenziemathislab.org/
+.. _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/
@@ -102,4 +104,3 @@ This plugin mainly uses the following libraries and software:
 .. [#] Mapping mesoscale axonal projections in the mouse brain using a 3D convolutional network, Friedmann et al., 2020 ( https://pnas.org/cgi/doi/10.1073/pnas.1918465117 )
 .. [#] The mesoSPIM initiative: open-source light-sheet microscopes for imaging cleared tissue, Voigt et al., 2019 ( https://doi.org/10.1038/s41592-019-0554-0 )
 .. [#] MONAI Project website ( https://monai.io/ )
-