We propose a twofold method that first automatically builds a statistical shape model of
anatomical 3D brain structures of interest, then uses this model for delineating structure …

Atlas-based segmentation has proven effective in multiple applications. Usually, several
reference images are combined to create a representative average atlas image …

An accurate labeling of a multi-part, complex anatomical structure (eg, brain) is required in
order to compare data across images for spatial analysis. It can be achieved by fitting an …

In medical image processing Segmentation of anatomical regions of the brain is the
fundamental problem. Here, a brain tumor segmentation method has been developed and …

Segmentation of brain images often requires a statistical atlas for providing prior information
about the spatial position of different structures. A major limitation of atlas-based …

Image segmentation is a main task in many medical applications such as surgical or
radiation therapy planning, automatic labelling of anatomical structures or morphological …

This paper addresses the problem of creating probabilistic brain atlases from manually
labeled training data. We propose a general mesh-based atlas representation, and compare …

A fully automatic algorithm is proposed to segment glioma MR sequences, by availing of the
complimentary information provided by multiple Magnetic Resonance (MR) sequences, and …

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 …

We present a novel fully-automated generative brain tumor segmentation method that
makes use of a widely available probabilistic brain atlas of white matter, grey matter and …