Abstract #3460

Using deep learning to generate missing anatomical imaging contrasts required for lesion segmentation in patients with glioma.

Ozan Genc¹, Pranathi Chunduru², Annette Molinaro¹, Valentina Pedoia¹, Susan Chang¹, Javier Villanueva-Meyer¹, and Janine Lupo Palladino¹

¹University of California San Francisco, San Francisco, CA, United States, ²Johnson & Johnson, San Francisco, CA, United States

Synopsis

Missing value imputation is an important concept in statistical analyses. We utilized conditional GAN based deep learning models to learn missing contrasts in MR images. We trained two deep learning models (FSE to FLAIR and T1 post GAD to T1 pre-GAD) for MR image conversion for missing value imputation. The model performances are evaluated by visual examination and comparing SSIM values. We observed that these models can learn the output contrast.

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