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NeuralSeg: the state of technology machine learning platform using deep learning to segment MRI images

NeuralSeg State of technology machine learning platform using deep learning to segments MRI images Developed by Nubinary

NeuralSeg: the state of technology machine learning platform using deep learning to segment MRI images

NeuralSeg State of technology machine learning platform using deep learning to segments MRI images Developed by Nubinary

Challenge

NeuralSeg is the state of technology machine learning platform that  automatically segments MRI images with the power of deep learning. NeuralSeg reduces the time required for MRI image segmentation from days of manual work to few minutes of machine work and the cost from thousands of dollars to a fraction. In its core, NeuralSeg’s algorithm uses GPU processing to perform the segmentation.

Challenge

NeuralSeg is the state of technology machine learning platform that automatically segments MRI images with the power of deep learning. NeuralSeg reduces the time required for MRI image segmentation from days of manual work to few minutes of machine work and the cost from thousands of dollars to a fraction. In its core, NeuralSeg’s algorithm uses GPU processing to perform the segmentation.

How

we helped

How

we helped

NuBinary Services - Cloud & DevOps Implementation
Cloud & DevOps Implementation
NuBinary Services - UI/UX Development
UI/UX Development
NuBinary Services - Roadmap Planning & Strategy
Roadmap Planning & Strategy
NuBinary Services - Grant Funding Support
Grant Funding Support
Team Building & Knowledge Transfer
Team Building & Knowledge Transfer
NuBinary Services - Cloud & DevOps Implementation
Cloud & DevOps Implementation
NuBinary Services - UI/UX Development
UI/UX Development
NuBinary Services - Roadmap Planning & Strategy
Roadmap Planning & Strategy
NuBinary Services - Grant Funding Support
Grant Funding Support

Solution

NeuralSeg’s machine learning algorithm is developed by researchers in McMaster University and the team engaged NuBinary to turn their scientific algorithm to a commercialized web-enabled and scalable platform deployed on cloud and easily accessible by their clients. NuBinary designed and implemented NeuralSeg platform’s web interface, user and image management system, heavy file uploading system, online MRI image preview, async backend job processing, job dispatching and scaling system as well as distributed cloud infrastructure for best cost efficiency, reliability and scalability.

Solution

NeuralSeg’s machine learning algorithm is developed by researchers in McMaster University and the team engaged NuBinary to turn their scientific algorithm to a commercialized web-enabled and scalable platform deployed on cloud and easily accessible by their clients. NuBinary designed and implemented NeuralSeg platform’s web interface, user and image management system, heavy file uploading system, online MRI image preview, async backend job processing, job dispatching and scaling system as well as distributed cloud infrastructure for best cost efficiency, reliability and scalability.

NeuralSeg State of technology machine learning platform using deep learning to segments MRI images Developed by Nubinary

Technology
Stack

  • Ruby on Rails and Google Cloud PostgreSQL on the backend
  • React.js front-end
  • Google Cloud file storage for large file upload support
  • Google Cloud compute instances and distributed Cloud GPU instances for processing parallelization
  • Docker containers
  • Continuous integration and delivery using CircleCI
  • Near-full automated test coverage on the backend and the front-end for highest quality

Technology
Stack

  • PHP Laravel
  • React.js front-end
  • Elasticsearch Big data storage and query engine
  • RabbitMQ for message caching and dispatching
  • Continuous Integration and Deployment
  • Auto scaling, load balanced and fault tolerant on DigitalOcean nodes

Tell us about your project, let’s take your business where it needs to be.

Tell us about your project, let’s take your business where it needs to be.