Nature

Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies

Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep ...
Nature

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

Neutron imaging 1 (NI), small-angle neutron scattering 2,3 (SANS) and ultra-small-angle neutron scattering 4 (USANS) modalities are used respectively for micro and meso-scale material characterization ...
Ophthalmology Times

Study finds mixed real and synthetic eye images enhance ocular AI performance

Training a computer vision model on a 50:50 blend of synthetic and real eye images produces more reliable segmentation of the ...
EurekAlert!

DeepWMH: a deep learning tool for accurate white matter hyperintensity segmentation without requiring manual annotations for training

White matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images are imaging features in various neurological diseases and essential markers for clinical impairment and ...