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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.