brainchop
cornerstone
| brainchop | cornerstone | |
|---|---|---|
| 16 | 1 | |
| 229 | 2,007 | |
| 4.8% | 0.5% | |
| 9.1 | 1.9 | |
| 21 days ago | about 2 months ago | |
| JavaScript | JavaScript | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
brainchop
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Release Radar • March 2024 Edition
We featured Brainchop back in the February 2023 Release Radar. Since then, Brainchop is back with a powerful model update. Brainchop is a 3D MRI rendering and segmentation tool for analysing and processing Magnetic Resonance Imaging (MRIs) of various brains. Using AI, the new version features three classes of models for processing and analysing images of brains. Here are the three new models:
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"[N]" Brainchop V1.4.0
Brainchop win TF Community Sportlight Award Github: https://github.com/neuroneural/brainchop
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Brainchop V1.4.0 is out: Rendering input in 3D and apply 3D image processing
You welcome, there is no backend with brainchop, but in-browser JS functions, please visit https://github.com/neuroneural/brainchop/tree/master/js/brainchop
- Brainchop v1.3.0: First in browser open source and free software for 3D brain segmentation. (Follow up)
- Brainchop: In-browser 3D MRI segmentation
- Brainchop: Volumetric Segmentation of brain 3D MRI images (Follow up)
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"[Project]" Brainchop: In Browser 3D Segmentation. Now 50 and 104 Brain Segmentations. (Follow up).
Live Demo: brainchop.org
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Brainchop: In Browser 3D Segmentation. And now more options with Pyodide. (Follow up).
We appreciate your ideas/feedback /comments by visit our discussion board and please spread a word about our work.
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Brainchop: In-browser deep learning framework for volumetric Segmentation
Live Demo: brainchop.org Brainchopis a client-side web-application for automatic segmentation of MRI volumes that brings automatic volumetric segmentation capability to neuroimaging by running a robustly pre-trained deep learning model. The app does not require technical sophistication from the user and is designed for locally and privately segmenting user’s T1 volumes. Results of the segmentation may be easily saved locally after the computation. An intuitive interactive interface that does not require any special training nor specific instruction to run enables access to a state of the art deep learning brain segmentation for anyone with a modern browser (e.g. Firefox, Chrome etc) and commonly available hardware. Additionally, we make implementation of brainchop freely available releasing its pure Javascript code as open-source.
Hi, and thanks for asking. currently we are planning to release a version with cortical structure of 50 labels within a week or so. We have also a subcortical one of 104 labels but we work on optimize it more to make it run with most browsers. Please keep tracking us, and feel free please to reach us out with your suggestion/feedback/question using Brainchop discussion board.
cornerstone
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Flutter medical image/file view DICOM File and doc,docx,pdf view/open in flutter app
You will likely have to write your own DICOM viewer in dart. A webview around a JavaScript DICOM viewer like https://github.com/cornerstonejs/cornerstone might be the way to go.
What are some alternatives?
three-js-games - 👾 The code for my Three.js game dev experiments on YouTube.
cornerstone3D - Cornerstone is a set of JavaScript libraries that can be used to build web-based medical imaging applications. It provides a framework to build radiology applications such as the OHIF Viewer.
dicomviewer - DICOM Viewer in Nextcloud
Vue.js - This is the repo for Vue 2. For Vue 3, go to https://github.com/vuejs/core
three-js-tutorials - 🥉 The code for my Three.js tutorial series on YouTube.
facial-emotion-recognition - This repository demonstrates an end-to-end pipeline for real-time Facial emotion recognition application through full-stack development. The frontend is developed in react.js and the backend is developed in FastAPI. The emotion prediction model is built with Tensorflow Keras, and for real-time face detection with animation on the frontend, Tensorflow.js have been used.
Viewers - OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages
dataset-uta4-dicom - [AVI 2020] UTA4: Medical Imaging DICOM files dataset.
prototype-cornerstone - CornerstoneJS Modified Prototype