Update README.md

README.md

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 <img src="http://www.cellpose.org/static/images/logo.png?raw=True" width="250" title="cellpose" alt="cellpose" align="right" vspace = "50">
 
 [![Documentation Status](https://readthedocs.org/projects/cellpose/badge/?version=latest)](https://cellpose.readthedocs.io/en/latest/?badge=latest)
-![tests](https://github.com/mouseland/cellpose/actions/workflows/test_and_deploy.yml/badge.svg)
-[![Coverage Status](https://coveralls.io/repos/github/MouseLand/cellpose/badge.svg?branch=master)](https://coveralls.io/github/MouseLand/cellpose?branch=master)
+[![tests](https://github.com/mouseland/cellpose/actions/workflows/test_and_deploy.yml/badge.svg)
+[![codecov](https://codecov.io/gh/MouseLand/cellpose/branch/main/graph/badge.svg?token=9FFo4zNtYP)](https://codecov.io/gh/MouseLand/cellpose)
 [![PyPI version](https://badge.fury.io/py/cellpose.svg)](https://badge.fury.io/py/cellpose)
 [![Downloads](https://pepy.tech/badge/cellpose)](https://pepy.tech/project/cellpose)
 [![Downloads](https://pepy.tech/badge/cellpose/month)](https://pepy.tech/project/cellpose)
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 A generalist algorithm for cell and nucleus segmentation (v1.0) that can be optimized for your own data (v2.0). 
 
-Cellpose was written by Carsen Stringer and Marius Pachitariu. To learn about Cellpose 2.0 (human-in-the-loop), read the [paper](https://www.nature.com/articles/s41592-022-01663-4) or watch the [talk](https://www.youtube.com/watch?v=3ydtAhfq6H0). To learn about Cellpose 1.0, read the [paper](https://t.co/kBMXmPp3Yn?amp=1) or watch the [talk](https://t.co/JChCsTD0SK?amp=1). For support, please open an [issue](https://github.com/MouseLand/cellpose/issues).  Please find the detailed documentation at <span style="font-size:larger;">[cellpose.readthedocs.io](https://cellpose.readthedocs.io/en/latest/)</span>.
+Cellpose was written by Carsen Stringer and Marius Pachitariu. To learn about Cellpose 2.0 (human-in-the-loop), read the [paper](https://www.nature.com/articles/s41592-022-01663-4) or watch the [talk](https://www.youtube.com/watch?v=3ydtAhfq6H0). To learn about Cellpose 1.0, read the [paper](https://t.co/kBMXmPp3Yn?amp=1) or watch the [talk](https://t.co/JChCsTD0SK?amp=1). For support, please open an [issue](https://github.com/MouseLand/cellpose/issues). 
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+Please see install instructions [below](README.md/#Installation), and also check out the detailed documentation at [**cellpose.readthedocs.io**](https://cellpose.readthedocs.io/en/latest/) for more information.
 
 ### CITATION
 
 **If you use Cellpose 1 or 2, please cite the Cellpose 1.0 [paper](https://t.co/kBMXmPp3Yn?amp=1):**  
 Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. <em>Nature methods, 18</em>(1), 100-106.
-[[bibtex](https://scholar.googleusercontent.com/scholar.bib?q=info:rmoKTp0cEiYJ:scholar.google.com/&output=citation&scisdr=CgXHFLYtEMb9qOfkmrg:AAGBfm0AAAAAY2jigrhA_p9qteLfyKDZlh96dZdapgkX&scisig=AAGBfm0AAAAAY2jigv55oXhgKwSArS2sr_fxBh--42gU&scisf=4&ct=citation&cd=-1&hl=en&scfhb=1)]
 
 **If you use the new human-in-the-loop training or use the new cyto2, livecell, or tissuenet models, please also cite the Cellpose 2.0 [paper](https://www.nature.com/articles/s41592-022-01663-4):**  
-Pachitariu, M. & Stringer, C. (2022). Cellpose 2.0: how to train your own model. <em>Nature methods</em>.
+Pachitariu, M. & Stringer, C. (2022). Cellpose 2.0: how to train your own model. <em>Nature methods</em>, 1-8.
 
 :triangular_flag_on_post: the new tissuenet and livecell models (`tissuenet`, `TN1`, `TN2`, `TN3`, `livecell`, `LC1`, `LC2`, `LC3` and `LC4`)  were trained using data under a **CC-BY-NC** license, so these models are **non-commercial use only**.
 
 ### :star2: v2.0 (April 2022) :star2:
 
 Cellpose 2.0 now allows human-in-the-loop training of models! To learn more...
 * Check out the twitter [thread](https://twitter.com/marius10p/status/1511415409047650307?s=20&t=umTVIG1CFKIWHYMrQqFKyQ) for an overview.
-* Check out the [paper](https://www.nature.com/articles/s41592-022-01663-4) for more details on the algorithm and the performance.
+* Check out the [paper](https://www.nature.com/articles/s41592-022-01663-4) for more details on the algorithm and the performance. Also, there's a short review of the paper available [here](https://www.nature.com/articles/s41592-022-01664-3).
 * Watch the short intro [talk](https://www.youtube.com/watch?v=3ydtAhfq6H0) and watch the longer [tutorial talk](https://youtu.be/5qANHWoubZU) which goes through running Cellpose 2.0 in the GUI and a jupyter notebook.
 * Check out the full human-in-the-loop [video](https://youtu.be/3Y1VKcxjNy4). 
 * Check out the colab notebook to get cloud access to a GPU to train your models or run your custom models: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MouseLand/cellpose/blob/main/notebooks/run_cellpose_2.ipynb).
@@ -169,10 +171,12 @@ You can now **drag and drop** any images (*.tif, *.png, *.jpg, *.gif) into the G
 2. Start the GUI with `python -m cellpose`.
 3. Drag an image from the folder into the GUI.
 4. Set the model (in demo all are `cyto`) and the channel you want to segment (in demo all are `green`). Optionally set the second channel if you are segmenting `cyto` and have an available nucleus channel.
-5. Click the `calibrate` button to estimate the size of the objects in the image. Alternatively you can set the `cell diameter` by hand and press ENTER. You will see the size you set as a red disk at the bottom left of the image.
+5. Click the `calibrate` button to estimate the size of the objects in the image. Alternatively (RECOMMENDED) you can set the `cell diameter` by hand and press ENTER. You will see the size you set as a red disk at the bottom left of the image.
 6. Click the `run segmentation` button. If MASKS ON is checked, you should see masks drawn on the image.
 7. Now you can click the LEFT/RIGHT arrow keys to move through the folder and segment another image.
 
+To draw ROIs on the image you can right-click then hover to complete the ROI (do not right-click and drag). To remove ROIs left-click while holding down CTRL. See more details [here](https://cellpose.readthedocs.io/en/latest/gui.html).
+
 On the demo images each of these steps should run in less than a few seconds on a standard laptop or desktop (with mkl working).
 
 ### 3D segmentation