Deep Learning approaches for automatic segmentation of organs from CT scans and MRI
are providing promising results, leading towards a revolution in the radiologists' workflow …

Objective To develop and validate a novel convolutional neural network (CNN) termed
“Super U-Net” for medical image segmentation. Methods Super U-Net integrates a dynamic …

A collaborative framework was initiated to establish a community resource of ground truth
segmentations from cardiac MRI. Multi-site, multi-vendor cardiac MRI datasets comprising …

Medical image segmentation requires consensus ground truth segmentations to be derived
from multiple expert annotations. A novel approach is proposed that obtains consensus …

Liver segmentation is a crucial step in surgical planning from computed tomography scans.
The possibility to obtain a precise delineation of the liver boundaries with the exploitation of …

Background Manual contouring remains the most laborious task in radiation therapy
planning and is a major barrier to implementing routine Magnetic Resonance Imaging (MRI) …

Pelvic floor dysfunction is common in women after childbirth and precise segmentation of
magnetic resonance images (MRI) of the pelvic floor may facilitate diagnosis and treatment …

We present a new algorithm, called local MAP STAPLE, to estimate from a set of multi-label
segmentations both a reference standard segmentation and spatially varying performance …

ABSTRACT BACKGROUND AND PURPOSE Segmentation of human brain structures is
crucial for the volumetric quantification of brain disease. Advances in algorithmic …

Purpose Automated segmentation could improve the efficiency of modeling‐based pelvic
organ prolapse (POP) evaluations. However, segmentation performance is limited by the …