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Abstract #3544

Data Augmentation with Conditional Generative Adversarial Networks for Improved Medical Image Segmentation

Gregory Kuling1, Matt Hemsley 1,2, Geoff Klein1, Philip Boyer3, and Marzyeh Ghassemi4
1Medical Biophysics, University of Toronto, Toronto, ON, Canada, 2Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada, 3Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada, 4Computer Science and Medicine, University of Toronto, Toronto, ON, Canada

Performance of machine learning models for medical image segmentation is often hindered by a lack of labeled training data. We present a method for data augmentation wherein additional training examples are synthesized using a conditional generative adversarial network (cGAN) conditioned on a ground truth segmentation mask. The mask is later used as a label during the segmentation task. Using a dataset of N=48 T2-weighted MR volumes of the prostate, our results demonstrate the mean DSC score of a U-Net prostate segmentation model increased from 0.74 to 0.76 when synthetic training images are included with real data.

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