Deep learning has been the subject of a significant amount of research interest in the
development of novel algorithms for deep learning algorithms and medical image …

Volumetric registration of brain MRI is routinely used in human neuroimaging, eg, to align
different MRI modalities, to measure change in longitudinal analysis, to map an individual to …

Curation of large, diverse MRI datasets via multi-institutional collaborations can help
improve learning of generalizable synthesis models that reliably translate source-onto target …

Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

Portable, low-field magnetic resonance imaging (LF-MRI) of the brain may facilitate point-of-
care assessment of patients with Alzheimer's disease (AD) in settings where conventional …

Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-
effective, sustainable with lower carbon emissions than superconducting high-field MRI …

Recent learning-based approaches have made astonishing advances in calibrated medical
imaging like computerized tomography (CT). Yet, they struggle to generalize in uncalibrated …

Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei,
nodules, metastases, and other units. Existing instance segmentation networks for such …

Abstract Study Objectives To use relatively noisy routinely collected clinical data (brain
magnetic resonance imaging (MRI) data, clinical polysomnography (PSG) recordings, and …

Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo. In
this domain, digital brain atlases are essential for subject-specific segmentation of …