Multiatlas‐based segmentation with preregistration atlas selection

Langerak, Thomas; Berendsen, Floris F.; Heide, Uulke A. van der; Kotte, A.N.T.J.; Pluim, Josien P. W.

doi:10.1118/1.4816654

Cited by 56 publications

(51 citation statements)

References 18 publications

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“…The main disadvantage of these approaches is that they do not always generalize well beyond the training data. A related, yet different technique involves applying clustering (Langerak et al, 2013; Shi et al, 2010), manifold learning (Cao et al, 2011b,a, 2012; Duc et al, 2013; Wolz et al, 2010a; Gao et al, 2014), or computing a minimum spanning tree on the atlases (Jia et al, 2012). These learning algorithms are employed to construct a structure on the space of training images, which yields the means to efficiently compute distances between the atlases and novel image(s), run registrations, and propagate manual labels.…”

Section: Survey Of Methodological Developmentsmentioning

confidence: 99%

“…These approaches introduce additional complexity to the system, but can outperform standard similarity measures in the atlas selection task. Another approach to increase the efficiency and accuracy of atlas selection utilizes clustering, where the atlases, possibly together with the novel image(s), are analyzed to identify clusters of similar cases using methods such as k-means (Nouranian et al, 2014), affinity propagation (Langerak et al, 2013) and Floyd’s algorithm (Wang et al, 2014b). Then, cluster representatives (or exemplars) are used for the initial search of the most relevant atlases.…”

Section: Survey Of Methodological Developmentsmentioning

confidence: 99%

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Multi-atlas segmentation of biomedical images: A survey

Iglesias

Sabuncu

2015

Medical Image Analysis

575

443

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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.

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Section: Survey Of Methodological Developmentsmentioning

confidence: 99%

Section: Survey Of Methodological Developmentsmentioning

confidence: 99%

Multi-atlas segmentation of biomedical images: A survey

Iglesias

Sabuncu

2015

Medical Image Analysis

575

443

View full text Add to dashboard Cite

show abstract

“…14, 15 Langerak et al 16 used multiatlas-based segmentation with a focus on improving the atlas fusion. A combination of atlas selection and performance estimation strategies was used in an iterative procedure.…”

Section: Introductionmentioning

confidence: 99%

The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images

et al. 2015

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“…Potential selection criteria are body mass index, age, pathology, clinical history, sex, and handedness (8). For example, agebased selection has been shown to be as effective as selection based on image similarity after affine transformation (16). One major disadvantage of using metainformation is that it is not suitable when dealing with the anatomical variability that occurs independently of the simple meta-information (8).…”

Section: Introductionmentioning

confidence: 99%