The development of new therapeutic strategies such as immune checkpoint inhibitors (ICIs)
and targeted therapies has increased the complexity of the treatment landscape for solid …

Background Since the introduction of TotalSegmentator CT, there has been demand for a
similar robust automated MRI segmentation tool that can be applied across all MRI …

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 …

Modern medical image registration approaches predict deformations using deep networks.
These approaches achieve state-of-the-art (SOTA) registration accuracy and are generally …

Medical image segmentation has recently demonstrated impressive progress with deep
neural networks, yet the heterogeneous modalities and scarcity of mask annotations limit the …

Neurofibromatosis Type 1 is a genetic disorder characterized by the development of
neurofibromas (NFs), which exhibit significant variability in size, morphology, and …

Liver cirrhosis, the end stage of chronic liver disease, is characterized by extensive bridging
fibrosis and nodular regeneration, leading to an increased risk of liver failure, complications …

We present an end-to-end deep learning framework for automated liver cirrhosis stage
estimation from multi-sequence MRI. Cirrhosis is the severe scarring (fibrosis) of the liver …

Objectives: To present a publicly available torso segmentation network for large
epidemiology datasets on volumetric interpolated breath-hold examination (VIBE) images …

Computed tomography (CT) segmentation models often contain classes that are not
currently supported by magnetic resonance imaging (MRI) segmentation models. In this …