{"id":13562663,"url":"https://github.com/neuroneural/brainchop","last_synced_at":"2025-05-15T22:12:13.266Z","repository":{"id":38281551,"uuid":"383217362","full_name":"neuroneural/brainchop","owner":"neuroneural","description":"Brainchop: In-browser 3D MRI rendering and segmentation","archived":false,"fork":false,"pushed_at":"2025-04-03T00:59:50.000Z","size":380544,"stargazers_count":425,"open_issues_count":2,"forks_count":53,"subscribers_count":8,"default_branch":"master","last_synced_at":"2025-05-14T04:09:54.456Z","etag":null,"topics":["3d-segmentation","deep-learning","frontend-app","javascript","medical-imaging","mri","mri-segmentation","neuroimaging","pyodide","tensorflowjs","three-js"],"latest_commit_sha":null,"homepage":"https://neuroneural.github.io/brainchop/","language":"JavaScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/neuroneural.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"docs/CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"docs/CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2021-07-05T17:27:40.000Z","updated_at":"2025-05-13T21:54:07.000Z","dependencies_parsed_at":"2024-06-01T22:28:15.034Z","dependency_job_id":"e86f2e51-fd8a-4b03-a2c2-91f31057f541","html_url":"https://github.com/neuroneural/brainchop","commit_stats":null,"previous_names":[],"tags_count":20,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neuroneural%2Fbrainchop","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neuroneural%2Fbrainchop/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neuroneural%2Fbrainchop/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neuroneural%2Fbrainchop/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/neuroneural","download_url":"https://codeload.github.com/neuroneural/brainchop/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254430335,"owners_count":22069909,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["3d-segmentation","deep-learning","frontend-app","javascript","medical-imaging","mri","mri-segmentation","neuroimaging","pyodide","tensorflowjs","three-js"],"created_at":"2024-08-01T13:01:10.936Z","updated_at":"2025-05-15T22:12:08.243Z","avatar_url":"https://github.com/neuroneural.png","language":"JavaScript","funding_links":[],"categories":["JavaScript"],"sub_categories":[],"readme":"\n# 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)\n\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://neuroneural.github.io/brainchop\"\u003e\n    \u003cimg width=\"100%\" src=\"https://github.com/neuroneural/brainchop/releases/download/v3.4.0/Banner.png\"\u003e\n  \u003c/a\u003e\n\n\n**Frontend For Neuroimaging.  Open Source**\n\n**[brainchop.org](https://neuroneural.github.io/brainchop) \u0026emsp;  [Updates](#Updates) \u0026emsp; [Doc](https://github.com/neuroneural/brainchop/wiki/) \u0026emsp; [News!](#News) \u0026emsp; [Cite](#Citation) \u0026emsp; [v3](https://neuroneural.github.io/brainchop/v3)**\n\n\u003c/div\u003e\n\n\n\u003cbr\u003e\n \u003cimg src=\"https://github.com/neuroneural/brainchop/blob/master/css/logo/brainchop_logo.png\"  width=\"25%\" align=\"right\"\u003e\n\n \u003cp align=\"justify\"\u003e\n \u003cb\u003e\u003ca href=\"https://neuroneural.github.io/brainchop/\"  style=\"text-decoration: none\"\u003e Brainchop\u003c/a\u003e\u003c/b\u003e brings automatic 3D MRI  volumetric segmentation  capability to neuroimaging  by running a lightweight deep learning model (e.g., \u003ca href=\"https://medium.com/pytorch/catalyst-neuro-a-3d-brain-segmentation-pipeline-for-mri-b1bb1109276a\" target=\"_blank\"  style=\"text-decoration: none\"\u003e MeshNet\u003c/a\u003e) in the web-browser for inference on the user side. \n \u003c/p\u003e\n\n \u003cp align=\"justify\"\u003e\n 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. \u003cb\u003e\u003ca href=\"v\"  style=\"text-decoration: none\"\u003eNiiVue\u003c/a\u003e\u003c/b\u003e viewer is integrated with the tool for MRI visualization.  For more information about Brainchop, please refer to this detailed \u003cb\u003e\u003ca href=\"https://github.com/neuroneural/brainchop/wiki/\"  style=\"text-decoration: none\"\u003eWiki\u003c/a\u003e\u003c/b\u003e and this \u003cb\u003e\u003ca href=\"https://trendscenter.org/in-browser-3d-mri-segmentation-brainchop-org/\"  style=\"text-decoration: none\"\u003e Blog\u003c/a\u003e\u003c/b\u003e.\n\n  For questions or to share ideas, please refer to our  \u003cb\u003e\u003ca href=\"https://github.com/neuroneural/brainchop/discussions/\"  style=\"text-decoration: none\"\u003e Discussions \u003c/a\u003e\u003c/b\u003e board.\n\n \u003c/p\u003e\n\n\u003cdiv align=\"center\"\u003e\n\n![Interface](https://github.com/neuroneural/brainchop/releases/download/v3.4.0/brainchop_Arch.png)\n\n**Brainchop high-level architecture**\n\u003c/div\u003e\n\n\n\u003cdiv align=\"center\"\u003e\n\n![Interface](https://github.com/neuroneural/brainchop/releases/download/v3.4.0/DL_Arch.png)\n\n**MeshNet deep learning architecture used for inference with Brainchop** (MeshNet  \u003ca href=\"https://arxiv.org/pdf/1612.00940.pdf\" target=\"_blank\"  style=\"text-decoration: none\"\u003e paper\u003c/a\u003e)\n\u003c/div\u003e\n\n\n## MeshNet Example\nThis basic example provides an overview of the training pipeline for the MeshNet model. \n\n* [![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)\n\n* [![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)\n\n\u003cbr\u003e\n\n## Live Demo\n\nTo see Brainchop **v4** in action please click  [here](https://neuroneural.github.io/brainchop). Or click on the gif below to see a video:\n\u003cdiv align=\"center\"\u003e\n  \n[![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)\n\u003c/div\u003e\n\nFor **v3** click [here](https://neuroneural.github.io/brainchop/v3).\n\n\u003cbr\u003e\n\n\n\n## Updates\n\n\u003cdiv align=\"center\"\u003e\n\n \u003cimg src=\"https://github.com/neuroneural/brainchop/releases/download/v4.0.0/Brainchop_Niivue.png\" width=\"100%\"\u003e\n\n**Brainchop \u003ca href= \"https://neuroneural.github.io/brainchop/\" target=\"_blank\"  style=\"text-decoration: none\"\u003e v4 \u003c/a\u003e with \u003ca href= \"https://github.com/niivue/niivue\" target=\"_blank\"  style=\"text-decoration: none\"\u003e NiiVue\u003c/a\u003e viewer**\n\u003c/div\u003e\n\n\u003cbr\u003e\n\n\u003cdiv align=\"center\"\u003e\n\n  \u003cimg src=\"https://github.com/neuroneural/brainchop/releases/download/v3.4.0/BrainchopMoreRobustModels.gif\"  width=\"60%\"\u003e\n\n**Brainchop \u003ca href= \"https://neuroneural.github.io/brainchop/v3\" target=\"_blank\"  style=\"text-decoration: none\"\u003e v3 \u003c/a\u003e with more robust models**\n\u003c/div\u003e\n\n\u003cbr\u003e\n\n\n\u003cdiv align=\"center\"\u003e\n\n![Interface](https://github.com/neuroneural/brainchop/releases/download/v3.4.0/Input3DEnhancements.gif)\n\n**Brainchop \u003ca href= \"https://neuroneural.github.io/brainchop/v3\" target=\"_blank\"  style=\"text-decoration: none\"\u003e v1.4.0 - v3.4.0 \u003c/a\u003e rendering MRI Nifti file in 3D**\n\u003c/div\u003e\n\n\u003cbr\u003e\n\n\u003cdiv align=\"center\"\u003e\n\n![Interface](https://github.com/neuroneural/brainchop/releases/download/v3.4.0/Brainchop3D.gif)\n\n\n**Brainchop \u003ca href= \"https://neuroneural.github.io/brainchop/v3\" target=\"_blank\"  style=\"text-decoration: none\"\u003e v1.3.0 - v3.4.0 \u003c/a\u003e  rendering segmentation output in 3D**\n\u003c/div\u003e\n\n\n\n\n\n## News!\n\n* 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).\n\n\u003cdiv align=\"center\"\u003e\n   \u003cimg src=\"https://github.com/neuroneural/brainchop/blob/master/css/news/ISBI_2024.jpeg\"  width=\"40%\"\u003e\n\u003c/div\u003e\n\n\u003cbr\u003e\n\u003cbr\u003e\n\n* Brainchop [paper](https://doi.org/10.21105/joss.05098) is published in the Journal of Open Source Software (JOSS) on March 28, 2023.\n\n\u003cdiv align=\"center\"\u003e\n   \u003ca href=\"https://doi.org/10.21105/joss.05098\"\u003e\u003cimg src=\"https://github.com/neuroneural/brainchop/blob/master/css/news/JOSS_Logo.png\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n\u003cbr\u003e\n\u003cbr\u003e\n\n* Brainchop abstract is accepted for poster presentation during the 2023 [OHBM](https://www.humanbrainmapping.org/) Annual Meeting.\n\n\u003cdiv align=\"center\"\u003e\n   \u003cimg src=\"https://github.com/neuroneural/brainchop/blob/master/css/news/OHBM_2023.jpeg\"  width=\"40%\"\u003e\n\u003c/div\u003e\n\n\u003cbr\u003e\n\u003cbr\u003e\n\n* Brainchop 1-page abstract and poster is accepted in 20th IEEE International Symposium on Biomedical Imaging ([ISBI 2023](https://2023.biomedicalimaging.org/en/))\n\n\u003cdiv align=\"center\"\u003e\n   \u003cimg src=\"https://github.com/neuroneural/brainchop/blob/master/css/news/ISBI_2023.png\"  width=\"40%\"\u003e\n\u003c/div\u003e\n\n\u003cbr\u003e\n\u003cbr\u003e\n\n* 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\u0026utm_medium=member_desktop) and [Twitter](https://twitter.com/TensorFlow/status/1572980019999264774)\n\n\u003cdiv align=\"center\"\u003e\n   \u003cimg src=\"https://github.com/neuroneural/brainchop/blob/master/css/news/TF_CommunityAward.png\"  width=\"60%\"\u003e\n\u003c/div\u003e\n\n\u003cbr\u003e\n\u003cbr\u003e\n\n* Brainchop  invited to [Pytorch](https://pytorch.org/ecosystem/ptc/2022) flag conference, New Orleans, Louisiana (Dec 2022) \n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/neuroneural/brainchop/blob/master/css/news/Pytorch_Poster.jpg\"  width=\"50%\"\u003e\n\u003c/div\u003e\n\n\n\u003cbr\u003e\n\u003cbr\u003e\n\n* Brainchop  invited to TensorFlow.js Show \u0026 Tell episode #7 (Jul 2022). \n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/neuroneural/brainchop/blob/master/css/news/TF_show_tell.png\"  width=\"50%\"\u003e\n\u003c/div\u003e\n\n## Citation\n\nBrainchop [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) \n\n\n\u003cbr\u003e\n\nFor **APA** style, the paper can be **cited** as: \n\n\u003e Masoud, M., Hu, F., \u0026 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\n\n\u003cbr\u003e\n\nFor **BibTeX** format that is used by some publishers,  please use: \n\n```BibTeX: \n@article{Masoud2023, \n  doi = {10.21105/joss.05098}, \n  url = {https://doi.org/10.21105/joss.05098}, \n  year = {2023}, \n  publisher = {The Open Journal}, \n  volume = {8}, \n  number = {83}, \n  pages = {5098}, \n  author = {Mohamed Masoud and Farfalla Hu and Sergey Plis}, \n  title = {Brainchop: In-browser MRI volumetric segmentation and rendering}, \n  journal = {Journal of Open Source Software} \n} \n```\n\u003cbr\u003e\n\nFor **MLA** style: \n\n\u003e 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.\n\n\u003cbr\u003e\n\nFor **IEEE** style:\n\n\u003e 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\n\n\n\u003cbr\u003e\n\n## Contribution and Authorship Guidelines\n\nIf 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:\n\n\u003e **\"Brainchop, originally developed by Mohamed Masoud and Sergey Plis (2023), was used in the development of this work.\"**\n\nWe also request that significant contributions to derivative works be recognized by including original authors as co-authors, where appropriate.\n\n\u003cbr\u003e\n\n## Funding\n\nThis work was funded by the NIH grant RF1MH121885. Additional support from NIH R01MH123610, R01EB006841 and NSF 2112455.\n\n\u003cbr /\u003e\n\u003cdiv align=\"center\"\u003e\n\n\u003cimg src='https://github.com/neuroneural/brainchop/blob/master/css/logo/TReNDS_logo.jpg' width='300' height='100'\u003e\u003c/img\u003e\n\n**Mohamed Masoud - Sergey Plis - 2024**\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneuroneural%2Fbrainchop","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fneuroneural%2Fbrainchop","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneuroneural%2Fbrainchop/lists"}