In addressing the challenge of creating a generalizable deep learning segmentation technique for magnetic resonance imaging (MRI), the UW-Madison Radiology AI team implemented an approach to seamlessly incorporate highly efficient image-to-image translation using adversarial learning into the segmentation algorithm. This novel technique SUSAN is now published in Magnetic Resonance in Medicine.
Segmentation is a fundamental step in medical image analysis. While there have been many deep learning methods addressing segmentation challenges in medical images, training highly efficient deep learning models typically requires a large number of training data, which could be extremely expensive and time-consuming. MRI has various image contrasts, making the standard approach of training deep learning models for individual image contrast inefficient and unscalable. Our method, SUSAN, standing for Segmenting Unannotated image Structure using Adversarial Network, was invented to segment different image contrasts using only one set of standard training data. SUSAN adheres to the smart AI philosophy to understand data more efficiently and use information cost-effectively.