Multi-atlas segmentation of biomedical images: A survey

Abstract

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.

A competitive strategy for atrial and aortic tract segmentation based on deformable models

“…Some examples are the atlasbased (Okada et al, 2015;Wolz et al, 2013;Xu et al, 2015), and statistical-based approaches (Yan et al, 2005;Yang et al, 2004). Despite the high versatility obtained, overlapping and merged regions were typically found, requiring post-processing techniques through mathematical morphology operations or refinement methodologies (Iglesias and Sabuncu, 2015).…”

Strengths and Pitfalls of Whole-Heart Atlas-Based Segmentation in Congenital Heart Disease Patients

“…Post-processing. The label fusion result does not necessarily represent the final segmentation; sometimes it is fed to another algorithm to estimate the output labels [3]. In whole-heart segmentation, this kind of post-processing techniques have not been that popular and are rarely used [7].…”

A Dynamic Non-Local Means Algorithm for Monochrome Image Denoising

“…Digital image processing technique is widely used in all kinds of fields, such as face recognition [1][2][3][4][5][6][7][8][9][10] and biomedical image processing [11][12][13]. The aim of digital image processing is to improve the potential information for human interpretation and processing of an image data for transmission, storage, and representation for autonomous machine perception.…”