Medical image segmentation is prerequisite in computer‐aided diagnosis. As the field
experiences tremendous paradigm changes since the introduction of foundation models …

Background: Segmentation of organs and structures in abdominal MRI is useful for many
clinical applications, such as disease diagnosis and radiotherapy. Current approaches have …

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 …

The liver is a frequent focus in radiology due to its diverse pathology, and artificial
intelligence (AI) could improve diagnosis and management. This systematic review aimed to …

Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types
acquired with different imaging protocols. The DICOM headers of these series often have …

John Mongan, MD, PhD, is an abdominal radiologist and associate chair for Translational
Informatics at UCSF. He chairs the RSNA Artificial Intelligence Committee; one of the …

Multi-parametric MRI (mpMRI) studies are widely available in clinical practice for the
diagnosis of various diseases. As the volume of mpMRI exams increases yearly, there are …

Accurate tumor segmentation within MRI images is of great importance for both diagnosis
and treatment; however, in many cases, sufficient annotated datasets may not be available …

Rationale and Objectives Multi-parametric MRI (mpMRI) studies of the body are routinely
acquired in clinical practice. However, a standardized naming convention for MRI protocols …

Accurate, automated MRI series identification is important for many applications, including
display (“hanging”) protocols, machine learning, and radiomics. The use of the series …