Generative augmentations for improved cardiac ultrasound segmentation using diffusion models.

ABSTRACT: One of the main challenges in current research on segmentation in cardiac ultrasound is the lack of large and varied labeled datasets and the differences in annotation conventions between datasets. This makes it difficult to design robust segmentation models that generalize well to external datasets. This work utilizes diffusion models to create generative augmentations that can significantly improve diversity of the dataset and thus the generalisability of segmentation models without the need for more annotated data. The generative augmentations are applied in addition to regular augmentations. A visual test survey showed that experts cannot clearly distinguish between real and fully generated images. Using the proposed generative augmentations, segmentation robustness was increased when

SUBMITTER: Van De Vyver G 

PROVIDER: S-EPMC12575775 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature