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https://github.com/neuroneural/brainchop
Brainchop: In-browser 3D MRI rendering and segmentation
https://github.com/neuroneural/brainchop
3d-segmentation deep-learning frontend-app javascript medical-imaging mri mri-segmentation neuroimaging pyodide tensorflowjs three-js
Last synced: 1 day ago
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Brainchop: In-browser 3D MRI rendering and segmentation
- Host: GitHub
- URL: https://github.com/neuroneural/brainchop
- Owner: neuroneural
- License: mit
- Created: 2021-07-05T17:27:40.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2024-11-24T22:26:30.000Z (about 1 month ago)
- Last Synced: 2024-12-22T18:04:19.437Z (9 days ago)
- Topics: 3d-segmentation, deep-learning, frontend-app, javascript, medical-imaging, mri, mri-segmentation, neuroimaging, pyodide, tensorflowjs, three-js
- Language: JavaScript
- Homepage: https://neuroneural.github.io/brainchop/
- Size: 363 MB
- Stars: 363
- Watchers: 7
- Forks: 46
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Contributing: docs/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: docs/CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# Brainchop []() []() [](https://github.com/neuroneural/brainchop/blob/master/LICENSE) [](https://github.com/neuroneural/brainchop/tree/master/models/mnm_tfjs_me_test) [](https://doi.org/10.21105/joss.05098)
**Frontend For Neuroimaging. Open Source**
**[brainchop.org](https://neuroneural.github.io/brainchop) [Updates](#Updates) [Doc](https://github.com/neuroneural/brainchop/wiki/) [News!](#News) [Cite](#Citation) [v3](https://neuroneural.github.io/brainchop/v3)**
Brainchop brings automatic 3D MRI volumetric segmentation capability to neuroimaging by running a lightweight deep learning model (e.g., MeshNet) in the web-browser for inference on the user side.
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. NiiVue viewer is integrated with the tool for MRI visualization. For more information about Brainchop, please refer to this detailed Wiki and this Blog.
For questions or to share ideas, please refer to our Discussions board.
**Brainchop high-level architecture**
**MeshNet deep learning architecture used for inference with Brainchop** (MeshNet paper)
## MeshNet Example
This basic example provides an overview of the training pipeline for the MeshNet model.
* [](https://colab.research.google.com/github/neuroneural/brainchop/blob/master/py2tfjs/MeshNet_Training_Example.ipynb) [MeshNet basic training example](./py2tfjs/MeshNet_Training_Example.ipynb)
* [](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)
## Live Demo
To see Brainchop **v4** in action please click [here](https://neuroneural.github.io/brainchop).
For **v3** click [here](https://neuroneural.github.io/brainchop/v3).
## Updates
**Brainchop v3 with more robust models**
**Brainchop v1.4.0 - v3.4.0 rendering MRI Nifti file in 3D**
**Brainchop v1.3.0 - v3.4.0 rendering segmentation output in 3D**
## 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) [](https://doi.org/10.21105/joss.05098)
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
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}
}
```
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.
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
## 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.
## Funding
This work was funded by the NIH grant RF1MH121885. Additional support from NIH R01MH123610, R01EB006841 and NSF 2112455.
**Mohamed Masoud - Sergey Plis - 2024**
