Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation.

ABSTRACT: Our contribution is a unified cross-modality feature disentagling approach for multi-domain image translation and multiple organ segmentation. Using CT as the labeled source domain, our approach learns to segment multi-modal (T1-weighted and T2-weighted) MRI having no labeled data. Our approach uses a variational auto-encoder (VAE) to disentangle the image content from style. The VAE constrains the style feature encoding to match a universal prior (Gaussian) that is assumed to span the styles of all the source and target modalities. The extracted image style is converted into a latent style scaling code, which modulates the generator to produce multi-modality images according to the target domain code from the image content features. Finally, we introduce a joint distribution matching disc

SUBMITTER: Jiang J 

PROVIDER: S-EPMC7757792 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature