Abstract #2421

Harmonization of multi-site T1 data using CycleGAN with segmentation loss (CycleGANs)

Suheyla Cetin-Karayumak¹, Evdokiya Knyazhanskaya², Brynn Vessey², Sylvain Bouix¹, Benjamin Wade³, David Tate⁴, Paul Sherman⁵, and Yogesh Rathi¹

¹Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States, ²Brigham and Women's Hospital, Boston, MA, United States, ³Ahmanson-Lovelace Brain Mapping Center, UCLA, Los Angeles, CA, United States, ⁴University of Utah, Salt Lake City, UT, United States, ⁵U.S. Air Force School of Aerospace Medicine, San Antonio, TX, United States

This study aims to tackle the structural MRI (T1) data harmonization problem by presenting a novel multi-site T1 data harmonization, which uses the CycleGAN network with segmentation loss (CycleGANs). CycleGANs aims to learn an efficient mapping of T1 data across scanners from the same set of subjects while simultaneously learning the mapping of free surfer parcellations. We demonstrated the efficacy of the method with the Dice overlap scores between FreeSurfer parcellations across two datasets before and after harmonization.

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