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NeuralSeg-logo1| Case Study


A state-of-the-art machine learning platform using deep learning to segment MRI images



NeuralSeg is a state-of-the-art 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 a few minutes of machine work and the cost from thousands of dollars to a fraction. At its core, NeuralSeg’s algorithm uses GPU processing to perform the segmentation.

How we helped

  • Cloud and DevOps implementation
  • UI/UX development
  • Grant funding support

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


NeuralSeg’s machine learning algorithm was developed by researchers at McMaster University. The team engaged NuBinary to turn their scientific algorithm into 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. We also distributed cloud infrastructure for best cost efficiency, reliability and scalability.

"NuBinary did an impeccable job building the web platform for my startup NeuralSeg. They provided prompt, professional, and thorough plans for product development that ensured we designed a scalable product that is ready for future growth."

Anthony Gatti, PhD
Founder & CEO, NeuralSeg


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