Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth

Understand

A lack of generalizability is one key limitation of deep learning based segmentation.

• Typically, one manually labels new training images when segmenting organs in different imaging modalities or segmenting abnormal organs from distinct disease cohorts.
• The manual efforts can be alleviated if one is able to reuse manual labels from one modality (e.g., MRI) to train a segmentation network for a new modality (e.g., CT).
• Previously, two stage methods have been proposed to use cycle generative adversarial networks (CycleGAN) to synthesize training images for a target modality.