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Abstract #3107

Synthetic harmonization of multi-site multi-vendor MRI data to improve MS lesion segmentation

Snehashis Roy1, Blake Dewey2, Peter Calabresi3, John A Butman4, and Dzung L Pham1

1Center for Neuroscience and Regenerative Medicine, Henry Jackson Foundation, Bethesda, MD, United States, 2Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States, 3Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 4Radiology and Imaging Sciences, National Institute of Health, Bethesda, MD, United States

Segmentation of lesions from magnetic resonance images of patients with multiple sclerosis is a challenging task, especially when involving multi-center or multi-scanner data. State-of-the-art lesion segmentation algorithms require training data to use identical acquisition protocols as the input data, but this is often difficult to control. In this work, we employ image synthesis to allow data from one scanner to resemble the data acquired in a different scanner. Overall lesion segmentation accuracy improves and the amount of false positives are reduced using synthesized images, indicating image synthesis can improve segmentation consistency in a heterogeneous dataset.

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