Case Study: University of North Carolina System speeds medical image processing with Google for Education and Techila on Google Cloud

At UNC, processing medical images accelerates from one week to three hours

The University of North Carolina System, working with Google for Education and Google Cloud Platform Compute Engine, needed a faster way to process large medical imaging datasets for research at UNC Chapel Hill. Their existing workflows for reconstructing high-resolution fMRI images were taking weeks, creating delays for researchers, clinicians, and patients.

Google for Education helped UNC deploy Techila’s distributed computation engine on Google Cloud to automatically scale and distribute jobs across many Compute Engine instances. The result was a dramatic speedup: image reconstruction time dropped from more than a month to about 18 hours, and feedback on test runs improved from one to two weeks to just three hours, helping researchers iterate and troubleshoot much faster.

University of North Carolina System

Wei-Tang Chang

Senior Research Associate