From 3c45eab062547f6aec577721c6917eb605195e53 Mon Sep 17 00:00:00 2001 From: mmasoud1 Date: Sat, 18 May 2024 02:03:30 -0400 Subject: [PATCH] Modify v3 readme --- v3/README.md | 178 +-------------------------------------------------- 1 file changed, 3 insertions(+), 175 deletions(-) diff --git a/v3/README.md b/v3/README.md index c6cf8d3..8861853 100644 --- a/v3/README.md +++ b/v3/README.md @@ -10,7 +10,7 @@ **Frontend For Neuroimaging. Open Source** -**[Demo](https://neuroneural.github.io/brainchop/v3)   [Updates](#Updates)   [Doc](https://github.com/neuroneural/brainchop/wiki/)   [News!](#News)   [Cite](#Citation)** +**[Demo](https://neuroneural.github.io/brainchop/v3)   [Doc](https://github.com/neuroneural/brainchop/wiki/)** @@ -25,184 +25,12 @@

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. Papaya viewer is integrated with the tool for MRI visualization. In version 1.3.0, Three.js is used for MRI 3D rendering. 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. - +

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- -![Interface](./css/images/brainchop_Arch.png) - -**Brainchop high-level architecture** -
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- -![Interface](./css/images/DL_Arch.png) - -**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. - -* [![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) - -
- -## Live Demo - -To see Brainchop v3.4.0 in action please click [here](https://neuroneural.github.io/brainchop/v3). - -
- -## Updates - -
- - - -**Brainchop v3.0.0 with more robust models** -
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- -![Interface](./css/images/Input3DEnhancements.gif) - -**Brainchop v1.4.0 rendering MRI Nifti file in 3D** -
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- -![Interface](./css/images/Brainchop3D.gif) - - -**Brainchop v1.3.0 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). - -
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- -* Brainchop [paper](https://doi.org/10.21105/joss.05098) is published in the Journal of Open Source Software (JOSS) on March 28, 2023. - -
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- -* Brainchop abstract is accepted for poster presentation during the 2023 [OHBM](https://www.humanbrainmapping.org/) Annual Meeting. - -
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- -* Brainchop 1-page abstract and poster is accepted in 20th IEEE International Symposium on Biomedical Imaging ([ISBI 2023](https://2023.biomedicalimaging.org/en/)) - -
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- -* 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) - -
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- -* Brainchop invited to [Pytorch](https://pytorch.org/ecosystem/ptc/2022) flag conference, New Orleans, Louisiana (Dec 2022) - -
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- -* Brainchop invited to TensorFlow.js Show & Tell episode #7 (Jul 2022). - -
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- -## 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) - - -
- -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 - - -
+> **Note:** The remaining content of this README file has been moved to the main [README.md](../README.md) file in the root of the repository. Please refer to the main README for general information, last updates, news, citations, usage guidelines, and contribution details. -## Funding -This work was funded by the NIH grant RF1MH121885. Additional support from NIH R01MH123610, R01EB006841 and NSF 2112455.