Multimodal fusion in neuroimaging combines data from multiple imaging modalities to
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …

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 …

Mindboggle (http://mindboggle. info) is an open source brain morphometry platform that
takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data …

Automated cardiac segmentation from magnetic resonance imaging datasets is an essential
step in the timely diagnosis and management of cardiac pathologies. We propose to tackle …

We introduce the Mindboggle-101 dataset, the largest and most complete set of free,
publicly accessible, manually labeled human brain images. To manually label the …

We present a novel approach to automatically segment magnetic resonance (MR) images of
the human brain into anatomical regions. Our methodology is based on a deep artificial …

All fields of neuroscience that employ brain imaging need to communicate their results with
reference to anatomical regions. In particular, comparative morphometry and group analysis …

We describe the construction of a digital brain atlas composed of data from manually
delineated MRI data. A total of 56 structures were labeled in MRI of 40 healthy, normal …

Normal and abnormal brains can be segmented by registering the target image with an
atlas. Here, an atlas is defined as the combination of an intensity image (template) and its …

Voxel-based morphometry from conventional T1-weighted images has proved effective to
quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate …