Abstract Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …

Atlas-based automated anatomical labeling is a fundamental tool in medical image
segmentation, as it defines regions of interest for subsequent analysis of structural and …

We propose a framework for the robust and fully-automatic segmentation of magnetic
resonance (MR) brain images called “Multi-Atlas Label Propagation with Expectation …

Multi-atlas segmentation provides a general purpose, fully-automated approach for
transferring spatial information from an existing dataset (“atlases”) to a previously unseen …

A new fusion strategy is introduced in this article to estimate state for multi-rate multi-sensor
systems with missing measurements. N sensors, which possess various sampling rates …

To date, label fusion methods have primarily relied either on global [eg, simultaneous truth
and performance level estimation (STAPLE), globally weighted vote] or voxelwise (eg …

Although agreement between the annotators who mark feature locations within images has
been studied in the past from a statistical viewpoint, little work has attempted to quantify the …

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

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 …