Multiatlas Segmentation as Nonparametric Regression

Awate, Suyash P.; Whitaker, Ross T.

doi:10.1109/tmi.2014.2321281

Cited by 27 publications

(18 citation statements)

References 28 publications

(48 reference statements)

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“…While the atlas selection method has a significant impact on segmentation performance, with notable exceptions (Awate and Whitaker, 2014; Heckemann et al, 2006), the optimal number of atlases to be selected seems to be an overlooked topic of research. Some algorithms simply choose the most suitable single atlas, and apply registration-based segmentation (Commowick and Malandain, 2007; Teng et al, 2010; Wu et al, 2007).…”

Section: Survey Of Methodological Developmentsmentioning

confidence: 99%

Multi-atlas segmentation of biomedical images: A survey

Iglesias

Sabuncu

2015

Medical Image Analysis

599

457

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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%

Multi-atlas segmentation of biomedical images: A survey

Iglesias

Sabuncu

2015

Medical Image Analysis

599

457

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“…This method required that the PA set and the target set used for atlas selection already have ground truth segmentations, which is not the case for the application presented here. Awate and Whitaker presented a method for quantifying the number of atlas images required to achieve a particular level of accuracy given the target dataset and atlas propagation methodology . Their method also relied on having a number of pre‐existing segmented images.…”

Section: Discussionmentioning

confidence: 99%

“…There are many variations on atlas-based segmentation methods. 4,5 The success of atlas-based segmentation is dependent on, among other factors, features of the application domain, 6 and both the quality of the initial expert segmentation and the quality of the registration of the atlas to the target images. 5,7 For application domains such as pelvic CT, low contrast borders can increase variability in manual segmentations between experts.…”

Section: Introductionmentioning

confidence: 99%

Similarity clustering‐based atlas selection for pelvic CT image segmentation

et al. 2019

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Purpose To demonstrate selection of a small representative subset of images from a pool of images comprising a potential atlas (PA) pelvic CT set to be used for autosegmentation of a separate target image set. The aim is to balance the need for the atlas set to represent anatomical diversity with the need to minimize resources required to create a high quality atlas set (such as multiobserver delineation), while retaining access to additional information available for the PA image set. Methods Preprocessing was performed for image standardization, followed by image registration. Clustering was used to select the subset that provided the best coverage of a target dataset as measured by postregistration image intensity similarities. Tests for clustering robustness were performed including repeated clustering runs using different starting seeds and clustering repeatedly using 90% of the target dataset chosen randomly. Comparisons of coverage of a target set (comprising 711 pelvic CT images) were made for atlas sets of five images (chosen from a PA set of 39 pelvic CT and MR images) (a) at random (averaged over 50 random atlas selections), (b) based solely on image similarities within the PA set (representing prospective atlas development), (c) based on similarities within the PA set and between the PA and target dataset (representing retrospective atlas development). Comparisons were also made to coverage provided by the entire PA set of 39 images. Results Exemplar selection was highly robust with exemplar selection results being unaffected by choice of starting seed with very occasional change to one of the exemplar choices when the target set was reduced. Coverage of the target set, as measured by best normalized cross‐correlation similarity of target images to any exemplar image, provided by five well‐selected atlas images (mean = 0.6497) was more similar to coverage provided by the entire PA set (mean = 0.6658) than randomly chosen atlas subsets (mean = 0.5977). This was true both of the mean values and the shape of the distributions. Retrospective selection of atlases (mean = 0.6497) provided a very small improvement over prospective atlas selection (mean = 0.6431). All differences were significant (P < 1.0E‐10). Conclusions Selection of a small representative image set from one dataset can be utilized to develop an atlas set for either retrospective or prospective autosegmentation of a different target dataset. The coverage provided by such a judiciously selected subset has the potential to facilitate propagation of numerous retrospectively defined structures, utilizing additional information available with multimodal imaging in the atlas set, without the need to create large atlas image sets.

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“…We note that for some applications, fuzzy segmentation approaches can be attractive to potentially better capture the inherent uncertainty in medical images, while others require discrete segmentations for subsequent analysis. Finally, another related approach is the formulation of multi-atlas segmentation as a nonparametric regression problem to estimate the expected error as a function of the number of atlases [1]. …”

Section: Discussionmentioning

confidence: 99%

Globally Optimal Label Fusion with Shape Priors

Oguz

Kashyap

Wang

et al. 2016

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

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Multi-atlas label fusion methods have gained popularity in a variety of segmentation tasks given their attractive performance. Graph-based segmentation methods are widely used given their global optimality guarantee. We propose a novel approach, GOLF, that combines the strengths of these two approaches. GOLF incorporates shape priors to the label-fusion problem and provides a globally optimal solution even for the multi-label scenario, while also leveraging the highly accurate posterior maps from a multi-atlas label fusion approach. We demonstrate GOLF for the joint segmentation of the left and right pairs of caudate, putamen, globus pallidus and nucleus accumbens. Compared to the FreeSurfer and FIRST approaches, GOLF is significantly more accurate on all reported indices for all 8 structures. We also present comparisons to a multi-atlas approach, which reveals further insights on the contributions of the different components of the proposed framework.

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Multiatlas Segmentation as Nonparametric Regression

Cited by 27 publications

References 28 publications

Multi-atlas segmentation of biomedical images: A survey

Multi-atlas segmentation of biomedical images: A survey

Similarity clustering‐based atlas selection for pelvic CT image segmentation

Globally Optimal Label Fusion with Shape Priors

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