  Multi-atlas segmentation (MAS), first introduced and popularized by the
pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh,
Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert
and Hammers (2006), is becoming one of the most widely-used and successful
image segmentation techniques in biomedical applications. By manipulating and
utilizing the entire dataset of "atlases" (training images that have been
previously labeled, e.g., manually by an expert), rather than some model-based
average representation, MAS has the flexibility to better capture anatomical
variation, thus offering superior segmentation accuracy. This benefit, however,
typically comes at a high computational cost. Recent advancements in computer
hardware and image processing software have been instrumental in addressing
this challenge and facilitated the wide adoption of MAS. Today, MAS has come a
long way and the approach includes a wide array of sophisticated algorithms
that employ ideas from machine learning, probabilistic modeling, optimization,
and computer vision, among other fields. This paper presents a survey of
published MAS algorithms and studies that have applied these methods to various
biomedical problems. In writing this survey, we have three distinct aims. Our
primary goal is to document how MAS was originally conceived, later evolved,
and now relates to alternative methods. Second, this paper is intended to be a
detailed reference of past research activity in MAS, which now spans over a
decade (2003 - 2014) and entails novel methodological developments and
application-specific solutions. Finally, our goal is to also present a
perspective on the future of MAS, which, we believe, will be one of the
dominant approaches in biomedical image segmentation.
