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Cited by 16Medical Image Segmentation TechniquesMedical Imaging Techniques and ApplicationsMedical Imaging and Analysis

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

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Wang, Hongkai, Stout, David B., Chatziioannou, Arion F.·2013·Medical Image Analysis
DOI 10.1016/j.media.2013.02.009PMC PMC3667217
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Abstract

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 ± 0.10mm 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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  • Other research employs 3D-2D registration incorporating a non-rigid transformation, parameterized using prior information or properties of length preservation of vessels.

    “Other research reported in the literature employs 3D–2D registration incorporating a non-rigid transformation. For example, the transformation can be parameterized using prior information such as the principal components and eigen-values of motion derived from a training dataset (Wang et al 2013) or properties of length preservation of vessels (Groher et al 2009).”

    From Robust 3D–2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation (2013) · cites this paper as Wang et al 2013

    Methods §2.1 describes a statistical mouse atlas built from training subjects, with shape-variation modes obtained by principal component analysis; Methods §2.2 uses those modes to constrain 3D deformation during 2D/3D registration. Our copy does not say that eigenvalues of motion parameterize the transformation. The vessel-length example is attributed to Groher et al., not Wang et al. (LLM, conf=0.82)

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Cited by

Sorted by citations

Authors
Wang, H., Stout, D. B., & Chatziioannou, A. F.
Affiliations
University of California, Los Angeles (US)
Corresponding author
  • Wang, Hongkai
  • Chatziioannou, Arion F.
ORCID
Wang, Hongkai
Venue
Medical Image Analysis
Publisher
Elsevier BV
Published
2013-05
Volume
17
Issue
4
Pages
401-416
ISSN
1361-8415 (ISSN-L)
DOI
10.1016/j.media.2013.02.009
PMC
PMC3667217
OpenAlex
W2051094927
Cited by
16
References
1
Citations by year
2025: 1 · 2024: 4 · 2023: 1 · 2022: 1 · 2019: 2 · 2018: 2 · 2017: 1 · 2016: 1 · 2015: 1 · 2014: 1 · 2013: 1
FWCI
1.50
Type
journal article
Language
en
Open access
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Medical Image Analysis (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Cancer Institute (U24CA092865, R24CA092865, P30CA016042)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
  • Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Keywords
statistical shape models, 2D-3D registration, micro-CT, X-ray projections, hierarchical registration, organ segmentation, small animal imaging, tumor-bearing mice
MeSH terms
Animals, Artificial Intelligence, Computer Simulation, Female, Male, Mice, Inbred C57BL, Models, Anatomic, Pattern Recognition, Automated, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Subtraction Technique, Tomography, X-Ray Computed, Reproducibility of Results, Imaging, Three-Dimensional, Mice

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