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brainchop

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Brainchop: In-browser 3D MRI rendering and segmentation

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# Brainchop  [![Version](https://img.shields.io/badge/Version-4.0.0-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)

**Frontend For Neuroimaging.  Open Source**

**[brainchop.org](https://neuroneural.github.io/brainchop) &emsp;  [Updates](#Updates) &emsp; [Doc](https://github.com/neuroneural/brainchop/wiki/) &emsp; [News!](#News) &emsp; [Cite](#Citation) &emsp; [v3](https://neuroneural.github.io/brainchop/v3)**

</div>

<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="v"  style="text-decoration: none">NiiVue</a></b> viewer is integrated with the tool for MRI visualization.  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>

![Interface](https://github.com/neuroneural/brainchop/releases/download/v3.4.0/brainchop_Arch.png)

**Brainchop high-level architecture**
</div>

![Interface](https://github.com/neuroneural/brainchop/releases/download/v3.4.0/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](https://raw.githubusercontent.com/neuroneural/brainchop/master/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 ](https://raw.githubusercontent.com/neuroneural/brainchop/master/py2tfjs/Convert_Trained_Model_To_TFJS.ipynb)

<br>

## Live Demo

To see Brainchop **v4** in action please click  [here](https://neuroneural.github.io/brainchop). Or click on the gif below to see a video:
<div align="center">
  
[![Brainchop Overhaul](https://github.com/neuroneural/brainchop/releases/download/v4.1.0/Brainchop_preview.gif)](https://github.com/neuroneural/brainchop/releases/download/v4.1.0/Brainchop_overhaul.mp4)
</div>

For **v3** click [here](https://neuroneural.github.io/brainchop/v3).

<br>

## Updates

**Brainchop <a href= "https://neuroneural.github.io/brainchop/" target="_blank"  style="text-decoration: none"> v4 </a> with <a href= "https://github.com/niivue/niivue" target="_blank"  style="text-decoration: none"> NiiVue</a> viewer**
</div>

<br>

**Brainchop <a href= "https://neuroneural.github.io/brainchop/v3" target="_blank"  style="text-decoration: none"> v3 </a> with more robust models**
</div>

<br>

![Interface](https://github.com/neuroneural/brainchop/releases/download/v3.4.0/Input3DEnhancements.gif)

**Brainchop <a href= "https://neuroneural.github.io/brainchop/v3" target="_blank"  style="text-decoration: none"> v1.4.0 - v3.4.0 </a> rendering MRI Nifti file in 3D**
</div>

<br>

![Interface](https://github.com/neuroneural/brainchop/releases/download/v3.4.0/Brainchop3D.gif)

**Brainchop <a href= "https://neuroneural.github.io/brainchop/v3" target="_blank"  style="text-decoration: none"> v1.3.0 - v3.4.0 </a>  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).

* Brainchop [paper](https://doi.org/10.21105/joss.05098) is published in the Journal of Open Source Software (JOSS) on March 28, 2023.

* Brainchop abstract is accepted for poster presentation during the 2023 [OHBM](https://www.humanbrainmapping.org/) Annual Meeting.

* Brainchop 1-page abstract and poster is accepted in 20th IEEE International Symposium on Biomedical Imaging ([ISBI 2023](https://2023.biomedicalimaging.org/en/))

* 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)

* Brainchop  invited to [Pytorch](https://pytorch.org/ecosystem/ptc/2022) flag conference, New Orleans, Louisiana (Dec 2022)

* Brainchop  invited to TensorFlow.js Show & Tell episode #7 (Jul 2022).

## 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>

## Contribution and Authorship Guidelines

If you modify or extend Brainchop in a derivative work intended for publication (such as a research paper or software tool), please cite and acknowledge the original Brainchop project and the original authors. Proper acknowledge should include the following:

> **"Brainchop, originally developed by Mohamed Masoud and Sergey Plis (2023), was used in the development of this work."**

We also request that significant contributions to derivative works be recognized by including original authors as co-authors, where appropriate.

<br>

## Funding

This work was funded by the NIH grant RF1MH121885. Additional support from NIH R01MH123610, R01EB006841 and NSF 2112455.

**Mohamed Masoud - Sergey Plis - 2024**
</div>

brainchop

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