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
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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