Abstract #3490

Simulation of brain deficits on MRI: A novel approach of ‘ground truth’ generation for machine learning

Kattie Sepehri¹, Xiaowei Song², Ryan Proulx³, Sujoy Ghosh Hajra⁴, Brennen Dobberthien⁵, Careesa Liu⁶, Ryan D'Arcy⁷, Don Murray³, and Andra Krauze⁵

¹UBC, Vancouver, BC, Canada, ²Surrey Memorial Hospital, Vancouver, BC, Canada, ³Safe Software, Vancouver, BC, Canada, ⁴National Research Council, Vancouver, BC, Canada, ⁵BC Cancer, Vancouver, BC, Canada, ⁶Baycrest Health Sciences Centre, Vancouver, BC, Canada, ⁷HealthTech Connex, Vancouver, BC, Canada

Robust machine learning algorithms for tumor identification require ground truth data sets. Ground truth data sets require expert input, are difficult and inefficient to produce. Feature Manipulation Engine (FME) allows for specific and complex data manipulation. We have created an FME workflow to produce simulated tumors that resemble realistic gliomas as rated by experts.

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