Atlas-based organ &amp;#x00026; bone approximation for ex-vivo &amp;#x03BC;MRI mouse data: A pilot study

Khmelinskii, Artem; Baiker, Martin; Chen, X.J.; Reiber, Johan H. C.; Henkelman, R. Mark; Lelieveldt, Boudewijn P. F.

doi:10.1109/isbi.2010.5490209

Cited by 4 publications

(7 citation statements)

References 11 publications

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“…The presented approaches for whole-body registration, change detection and multi-modal fusion represent a first step towards 3D integrated analysis of whole-body small animal follow-up studies.Validation studies reported elsewhere demonstrate high robustness of the developed registration methods [10,12,18,19], and end-users have reported benefits of the developed system in translational cancer research [3,4]. However, there are still many improvements that can be made towards minimizing the necessary user interaction, especially in multi-modal articulated registration.…”

Section: Discussionmentioning

confidence: 76%

“…The challenge of image registration in the presence of high postural variability has recently received increasing attention ( [5][6][7][8][9][10][11][12], see [12] for a detailed review of related work). We have developed an approach that uses explicit knowledge on skeletal degrees of freedom to constrain the registration process [12].…”

Section: Whole-body Registrationmentioning

confidence: 99%

“…A semi-automated generalization of this method towards MRI data was described in [10]. Suh et al developed a similar articulated registration approach for automated quantification of SPECT -CT data [11] 4.…”

Section: Change Quantificationmentioning

confidence: 99%

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Towards integrated analysis of longitudinal whole-body small animal imaging studies

Lelieveldt

Botha

Kaijzel

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper discusses a number of image analysis challenges emerging from longitudinal small animal molecular imaging studies. Three steps towards a quantitative 3D analysis of follow-up small animal imaging are presented: whole-body articulated registration, change visualization in follow-up data and fusion of optical and 3D structural imaging data.Several application examples are presented in the context of translational cancer research.

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Section: Discussionmentioning

confidence: 76%

Section: Whole-body Registrationmentioning

confidence: 99%

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Towards integrated analysis of longitudinal whole-body small animal imaging studies

Lelieveldt

Botha

Kaijzel

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…To serve these purposes, the registration of a mouse atlas to individual subjects is generally required to provide organ-level anatomical references. Various whole-body scale mouse atlases (Dogdas et al, 2007; Johnson et al, 2002; Khmelinskii et al, 2011a; Segars et al, 2004) have been developed and several approaches have been proposed to register these atlases with tomographic images like micro computed tomography (micro-CT) (Baiker et al, 2010; Baiker et al, 2011; Wang et al, 2012a), micro magnetic resonance imaging (micro-MR) (Khmelinskii et al, 2010), micro positron emission tomography (micro-PET) (Kesner et al, 2006) and micro single photon emission tomography (micro-SPECT) (Khmelinskii et al, 2011b). …”

Section: Introductionmentioning

confidence: 99%

A method of 2D/3D registration of a statistical mouse atlas with a planar X-ray projection and an optical photo

Wang

Stout

Chatziioannou

2013

Medical Image Analysis

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The development of sophisticated and high throughput whole body small animal imaging technologies has created a need for improved image analysis and increased automation. The registration of a digital mouse atlas to individual images is a prerequisite for automated organ segmentation and uptake quantification. This paper presents a fully-automatic method for registering a statistical mouse atlas with individual subjects based on an anterior-posterior X-ray projection and a lateral optical photo of the mouse silhouette. The mouse atlas was trained as a statistical shape model based on 83 organ-segmented micro-CT images. For registration, a hierarchical approach is applied which first registers high contrast organs, and then estimates low contrast organs based on the registered high contrast organs. To register the high contrast organs, a 2D-registration-back-projection strategy is used that deforms the 3D atlas based on the 2D registrations of the atlas projections. For validation, this method was evaluated using 55 subjects of preclinical mouse studies. The results showed that this method can compensate for moderate variations of animal postures and organ anatomy. Two different metrics, the Dice coefficient and the average surface distance, were used to assess the registration accuracy of major organs. The Dice coefficients vary from 0.31±0.16 for the spleen to 0.88±0.03 for the whole body, and the average surface distance varies from 0.54±0.06 mm for the lungs to 0.85±0.10 mm for the skin. The method was compared with a direct 3D deformation optimization (without 2D-registration-back-projection) and a single-subject atlas registration (instead of using the statistical atlas). The comparison revealed that the 2D-registration-back-projection strategy significantly improved the registration accuracy, and the use of the statistical mouse atlas led to more plausible organ shapes than the single-subject atlas. This method was also tested with shoulder xenograft tumor-bearing mice, and the results showed that the registration accuracy of most organs was not significantly affected by the presence of shoulder tumors, except for the lungs and the spleen.

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“…The sub-volumes of interest are obtained by segmenting one of the whole-body LR datasets using the semi-automatic whole-body articulated atlas-based method, first presented in [11] and applying the articulated planar reformation algorithm [8] which maps the data to a standardized atlas space. The contributions of this paper are twofold:…”

Section: Introductionmentioning

confidence: 99%

Super-resolution reconstruction of whole-body MRI mouse data: An interactive approach

Khmelinskii

Plenge

Kok

et al. 2012

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

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Super-resolution reconstruction (SRR) is a post-acquisition method for producing a high-resolution (HR) image from a set of lowresolution (LR) images. However, for large volumes of data, this technique is computationally very demanding and time consuming. In this study we focus on the specific case of whole-body mouse data and present a novel, integrated, end-to-end approach to overcome this problem. We combine articulated atlas-based segmentation and planar reformation techniques with state-of-the-art in SRR to produce high resolution, interactively selected, localized isotropic volumes-of-interest in whole-body mouse MRI. With this method we overcome time and memory related limitations when applying the SRR algorithm to the entire dataset, enabling interactive visualization and exploration of anatomical structures of interest in whole-body MRI mouse data on a normal desktop PC.

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Atlas-based organ & bone approximation for ex-vivo μMRI mouse data: A pilot study

Cited by 4 publications

References 11 publications

Towards integrated analysis of longitudinal whole-body small animal imaging studies

Towards integrated analysis of longitudinal whole-body small animal imaging studies

A method of 2D/3D registration of a statistical mouse atlas with a planar X-ray projection and an optical photo

Super-resolution reconstruction of whole-body MRI mouse data: An interactive approach

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