Update README.md (#67)

README.md

@@ -1,19 +1,41 @@
-# napari-cellseg3D: a napari plug-in for direct 3D cell segmentation with deep learning
-
+# CellSeg3D: self-supervised (and supervised) 3D cell segmentation for lightsheet microscopy
 <img src="https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/838605d0-9723-4e43-83cd-6dbfe4adf36b/cellseg-logo.png?format=1500w" title="cellseg3d" alt="cellseg3d logo" width="350" align="right" vspace = "80"/>
 
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-A napari plugin for 3D cell segmentation: training, inference, and data review. In particular, this project was developed for analysis of mesoSPIM-acquired (cleared tissue + lightsheet) datasets.
+- A napari plugin for 3D cell segmentation: training, inference, and data review. In particular, this project was developed for analysis of mesoSPIM-acquired (cleared tissue + lightsheet) datasets.
 
-**Help us make the code better by reporting issues and adding your feature requests!**
+![demo](https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/0d16a71b-3ff2-477a-9d83-18d96cb1ce28/full_demo.gif?format=500w)
+
+## Installation
+
+ 💻 See the [Installation page] in the documentation for detailed instructions.
+
+## Documentation
+
+📚 A lot of documentation is available at https://AdaptiveMotorControlLab.github.io/CellSeg3d
 
-----------------------------------
+You can also generate docs by running ``make html`` in the docs/ folder.
+
+## Quick Start
+
+To use the plugin, please run:
+```
+napari
+```
+Then go into Plugins > napari-cellseg3d, and choose which tool to use.
+
+- **Review (label)**: 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.
+- **Inference**: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells and compute statistics.
+- **Train**:  This module allows you to train segmentation algorithms from labeled volumes.
+- **Utilities**: This module allows you to perform several actions like cropping your volumes and labels dynamically, by selecting a fixed size volume and moving it around the image; computing prediction scores from ground truth and predicition labels; or converting labels from instance to segmentation and the opposite.
 
 ## News
 
@@ -24,9 +46,9 @@ A napari plugin for 3D cell segmentation: training, inference, and data review.
 Previous additions :
 
 - Improved training interface
-- Unsupervised model : WNet
-  - Generate labels directly from raw data !
-  - Can be trained in napari directly or in Colab
+- Unsupervised model : WNet3D
+  - Generate labels directly from raw data!
+  - Can be trained in napari directly or in Google Colab
   - Pretrained weights for mesoSPIM whole-brain cell segmentation
 - WandB support (install wandb and login to use automatically when training)
 - Remade and improved documentation
@@ -35,15 +57,9 @@ Previous additions :
 - New utilities
 - Many small improvements and many bug fixes
 
-## Demo
 
-![demo](https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/0d16a71b-3ff2-477a-9d83-18d96cb1ce28/full_demo.gif?format=500w)
-
-## Installation
 
-See the [Installation page] in the documentation for detailed instructions.
-
-### M1 Mac users
+### Install note for M1/M2 Mac users
 
 To avoid issues when installing on the ARM64 architecture, please follow these steps.
 
@@ -64,24 +80,6 @@ To avoid issues when installing on the ARM64 architecture, please follow these s
 
    OR directly via [napari-hub] (see Installation section above)
 
-## Documentation
-
-Available at https://AdaptiveMotorControlLab.github.io/CellSeg3d
-
-You can also generate docs by running ``make html`` in the docs/ folder.
-
-## Usage
-
-To use the plugin, please run:
-```
-napari
-```
-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.
-- **Inference**: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells and compute statistics.
-- **Train**:  This module allows you to train segmentation algorithms from labeled volumes.
-- **Utilities**: This module allows you to perform several actions like cropping your volumes and labels dynamically, by selecting a fixed size volume and moving it around the image; computing prediction scores from ground truth and predicition labels; or converting labels from instance to segmentation and the opposite.
 
 ## Requirements
 
@@ -96,6 +94,9 @@ If you get errors from MONAI regarding missing readers, please see [MONAI's opti
 
 ## Issues
 
+**Help us make the code better by reporting issues and adding your feature requests!**
+
+
 If you encounter any problems, please [file an issue] along with a detailed description.
 
 ## Testing
@@ -147,8 +148,8 @@ Distributed under the terms of the [MIT] license.
 
 ## Acknowledgements
 
-This plugin was developed by Cyril Achard, Maxime Vidal, Mackenzie Mathis.
-This work was funded, in part, from the Wyss Center to the [Mathis Laboratory of Adaptive Motor Control](https://www.mackenziemathislab.org/).
+This plugin was developed by originally Cyril Achard, Maxime Vidal, Mackenzie Mathis.
+This work was funded, in part, from the Wyss Center to the [Mathis Laboratory of Adaptive Intelligence](https://www.mackenziemathislab.org/).
 Please refer to the documentation for full acknowledgements.
 
 ## Plugin base