Data-driven simulations for training AI-based segmentation of neutron images.

ABSTRACT: Neutron interferometry uniquely combines neutron imaging and scattering methods to enable characterization of multiple length scales from 1 nm to 10 µm. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on the number of measured images per sample. Experiment time-constraints yield small quantities of measured images that are insufficient for automating image analyses using supervised artificial intelligence (AI) models. One approach alleviates this problem by supplementing annotated measured images with synthetic images. To this end, we create a data-driven simulation framework that supplements training data beyond typical data-driven augmentations by leveraging statistical intensity models, such as the Johnson family of

SUBMITTER: Sathe PS 

PROVIDER: S-EPMC10951284 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature