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diff --git a/v3/LICENSE b/v3/LICENSE
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+MIT License
+
+Copyright (c) 2021 neuroneural/brainchop
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/v3/README.md b/v3/README.md
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+
+# Brainchop  [![Version](https://img.shields.io/badge/Version-2.1.0-brightgreen)]() [![JS ](https://img.shields.io/badge/Types-JavaScript-blue)]() [![MIT-License ](https://img.shields.io/badge/license-MIT-green)](https://github.com/neuroneural/brainchop/blob/master/LICENSE) [![tfjs](https://img.shields.io/badge/tfjs-Pre--trained%20Model-blue)](https://github.com/neuroneural/brainchop/tree/master/models/mnm_tfjs_me_test) [![DOI](https://joss.theoj.org/papers/10.21105/joss.05098/status.svg)](https://doi.org/10.21105/joss.05098)
+
+
+<div align="center">
+  <a href="https://neuroneural.github.io/brainchop">
+    <img width="100%" src="https://github.com/neuroneural/brainchop/blob/master/css/images/Banner.png">
+  </a>
+
+
+**Frontend For Neuroimaging.  Open Source**
+
+**[Demo](https://neuroneural.github.io/brainchop) &emsp;  [Updates](#Updates) &emsp; [Doc](https://github.com/neuroneural/brainchop/wiki/) &emsp; [News!](#News) &emsp; [Cite](#Citation)**
+
+</div>
+
+
+<br>
+ <img src="https://github.com/neuroneural/brainchop/blob/master/css/logo/brainchop_logo.png"  width="25%" align="right">
+
+ <p align="justify">
+ <b><a href="https://neuroneural.github.io/brainchop/"  style="text-decoration: none"> Brainchop</a></b> brings automatic 3D MRI  volumetric segmentation  capability to neuroimaging  by running a lightweight deep learning model (e.g., <a href="https://medium.com/pytorch/catalyst-neuro-a-3d-brain-segmentation-pipeline-for-mri-b1bb1109276a" target="_blank"  style="text-decoration: none"> MeshNet</a>) in the web-browser for inference on the user side. 
+ </p>
+
+ <p align="justify">
+ We make the implementation of Brainchop freely available, releasing its pure javascript code as open-source. The user interface (UI)  provides a web-based end-to-end solution for 3D MRI segmentation. <b><a href="https://rii-mango.github.io/Papaya/"  style="text-decoration: none">Papaya</a></b> viewer is integrated with the tool for MRI visualization. In version 1.3.0, <b><a href="https://threejs.org/"  style="text-decoration: none">Three.js</a></b> is used for MRI 3D rendering.  For more information about Brainchop, please refer to this detailed <b><a href="https://github.com/neuroneural/brainchop/wiki/"  style="text-decoration: none">Wiki</a></b> and this <b><a href="https://trendscenter.org/in-browser-3d-mri-segmentation-brainchop-org/"  style="text-decoration: none"> Blog</a></b>.
+
+  For questions or to share ideas, please refer to our  <b><a href="https://github.com/neuroneural/brainchop/discussions/"  style="text-decoration: none"> Discussions </a></b> board.
+
+ </p>
+
+<div align="center">
+
+![Interface](https://github.com/neuroneural/brainchop/blob/master/css/images/brainchop_Arch.png)
+
+**Brainchop high-level architecture**
+</div>
+
+
+<div align="center">
+
+![Interface](https://github.com/neuroneural/brainchop/blob/master/css/images/DL_Arch.png)
+
+**MeshNet deep learning architecture used for inference with Brainchop** (MeshNet  <a href="https://arxiv.org/pdf/1612.00940.pdf" target="_blank"  style="text-decoration: none"> paper</a>)
+</div>
+
+
+## MeshNet Example
+This basic example provides an overview of the training pipeline for the MeshNet model. 
+
+* [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuroneural/brainchop/blob/master/py2tfjs/MeshNet_Training_Example.ipynb) [MeshNet basic training example](./py2tfjs/MeshNet_Training_Example.ipynb)
+
+* [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuroneural/brainchop/blob/master/py2tfjs/Convert_Trained_Model_To_TFJS.ipynb) [Convert the trained MeshNet model to tfjs model example ](./py2tfjs/Convert_Trained_Model_To_TFJS.ipynb)
+
+<br>
+
+## Live Demo
+
+To see Brainchop in action please click  [here](https://neuroneural.github.io/brainchop).
+
+<br>
+
+## Updates
+
+<div align="center">
+
+  <img src="https://github.com/neuroneural/brainchop/blob/master/css/images/BrainchopMoreRobustModels.gif"  width="60%">
+
+**Brainchop v3.0.0 with more robust models**
+</div>
+
+<br>
+
+
+<div align="center">
+
+![Interface](https://github.com/neuroneural/brainchop/blob/master/css/images/Input3DEnhancements.gif)
+
+**Brainchop v1.4.0 rendering MRI Nifti file in 3D**
+</div>
+
+<br>
+
+<div align="center">
+
+![Interface](https://github.com/neuroneural/brainchop/blob/master/css/images/Brainchop3D.gif)
+
+
+**Brainchop v1.3.0 rendering segmentation output in 3D**
+</div>
+
+
+
+
+
+## News!
+
+* Brainchop [v2.2.0](https://github.com/neuroneural/brainchop/releases/tag/v2.2.0) paper is accepted in the 21st IEEE International Symposium on Biomedical Imaging ([ISBI 2024](https://biomedicalimaging.org/2024/)). Lengthy arXiv version can be found [here](https://arxiv.org/abs/2310.16162).
+
+<div align="center">
+   <img src="https://github.com/neuroneural/brainchop/blob/master/css/news/ISBI_2024.jpeg"  width="40%">
+</div>
+
+<br>
+<br>
+
+* Brainchop [paper](https://doi.org/10.21105/joss.05098) is published in the Journal of Open Source Software (JOSS) on March 28, 2023.
+
+<div align="center">
+   <a href="https://doi.org/10.21105/joss.05098"><img src="https://github.com/neuroneural/brainchop/blob/master/css/news/JOSS_Logo.png"></a>
+</div>
+
+<br>
+<br>
+
+* Brainchop abstract is accepted for poster presentation during the 2023 [OHBM](https://www.humanbrainmapping.org/) Annual Meeting.
+
+<div align="center">
+   <img src="https://github.com/neuroneural/brainchop/blob/master/css/news/OHBM_2023.jpeg"  width="40%">
+</div>
+
+<br>
+<br>
+
+* Brainchop 1-page abstract and poster is accepted in 20th IEEE International Symposium on Biomedical Imaging ([ISBI 2023](https://2023.biomedicalimaging.org/en/))
+
+<div align="center">
+   <img src="https://github.com/neuroneural/brainchop/blob/master/css/news/ISBI_2023.png"  width="40%">
+</div>
+
+<br>
+<br>
+
+* Google, Tensorflow community spotlight award for brainchop (Sept 2022) on [Linkedin](https://www.linkedin.com/posts/tensorflow-community_github-neuroneuralbrainchop-brainchop-activity-6978796859532181504-cfCW?utm_source=share&utm_medium=member_desktop) and [Twitter](https://twitter.com/TensorFlow/status/1572980019999264774)
+
+<div align="center">
+   <img src="https://github.com/neuroneural/brainchop/blob/master/css/news/TF_CommunityAward.png"  width="60%">
+</div>
+
+<br>
+<br>
+
+* Brainchop  invited to [Pytorch](https://pytorch.org/ecosystem/ptc/2022) flag conference, New Orleans, Louisiana (Dec 2022) 
+
+<div align="center">
+  <img src="https://github.com/neuroneural/brainchop/blob/master/css/news/Pytorch_Poster.jpg"  width="50%">
+</div>
+
+
+<br>
+<br>
+
+* Brainchop  invited to TensorFlow.js Show & Tell episode #7 (Jul 2022). 
+
+<div align="center">
+  <img src="https://github.com/neuroneural/brainchop/blob/master/css/news/TF_show_tell.png"  width="50%">
+</div>
+
+## Citation
+
+Brainchop [paper](https://doi.org/10.21105/joss.05098) for v2.1.0 is published on March 28, 2023, in the Journal of Open Source Software (JOSS) [![DOI](https://joss.theoj.org/papers/10.21105/joss.05098/status.svg)](https://doi.org/10.21105/joss.05098) 
+
+
+<br>
+
+For **APA** style, the paper can be **cited** as: 
+
+> Masoud, M., Hu, F., & Plis, S. (2023). Brainchop: In-browser MRI volumetric segmentation and rendering. Journal of Open Source Software, 8(83), 5098. https://doi.org/10.21105/joss.05098
+
+<br>
+
+For **BibTeX** format that is used by some publishers,  please use: 
+
+```BibTeX: 
+@article{Masoud2023, 
+  doi = {10.21105/joss.05098}, 
+  url = {https://doi.org/10.21105/joss.05098}, 
+  year = {2023}, 
+  publisher = {The Open Journal}, 
+  volume = {8}, 
+  number = {83}, 
+  pages = {5098}, 
+  author = {Mohamed Masoud and Farfalla Hu and Sergey Plis}, 
+  title = {Brainchop: In-browser MRI volumetric segmentation and rendering}, 
+  journal = {Journal of Open Source Software} 
+} 
+```
+<br>
+
+For **MLA** style: 
+
+> Masoud, Mohamed, Farfalla Hu, and Sergey Plis. ‘Brainchop: In-Browser MRI Volumetric Segmentation and Rendering’. Journal of Open Source Software, vol. 8, no. 83, The Open Journal, 2023, p. 5098, https://doi.org10.21105/joss.05098.
+
+<br>
+
+For **IEEE** style:
+
+> M. Masoud, F. Hu, and S. Plis, ‘Brainchop: In-browser MRI volumetric segmentation and rendering’, Journal of Open Source Software, vol. 8, no. 83, p. 5098, 2023. doi:10.21105/joss.05098
+
+
+<br>
+
+## Funding
+
+This work was funded by the NIH grant RF1MH121885. Additional support from NIH R01MH123610, R01EB006841 and NSF 2112455.
+
+<br />
+<div align="center">
+
+<img src='https://github.com/neuroneural/brainchop/blob/master/css/logo/TReNDS_logo.jpg' width='300' height='100'></img>
+
+**Mohamed Masoud - Sergey Plis - 2024**
+</div>