Evidence map›Paper›PMID 23542374›Full record

ArticleMedical image analysis2013

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

Hongkai Wang, David B Stout, Arion F Chatziioannou

Abstract read
In one paragraph

Article in Medical image analysis, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. A hybrid registration-based method for whole-body micro-CT mice images.Medical & biological engineering & computing · 2016
    Article
  6. Article
  7. A deformable atlas of the laboratory mouse.Molecular imaging and biology · 2015
    Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hongkai WangDepartment of Molecular and Medical Pharmacology, Crump Institute for Molecular Imaging, University of California, Los Angeles, CA, USA. wang.hongkai@gmail.com
David B Stout
Arion F Chatziioannou

Funding

Women's CancersP30CA016042 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Samuel Wheeler French · 1985 to 2026
$134.5M
UCLA Imaging Resource for Mouse Cancer ModelsR24CA092865 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PHELPS, MICHAEL E · 2001 to 2006
$4.3M
The UCLA Imaging Resource for Mouse Cancer ModelsU24CA092865 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CHATZIIOANNOU, ARION XENOFON, PHELPS, MICHAEL E · 2007 to 2011
$2.2M
NCI NIH HHS 2U24 CA092865NCI NIH HHS P30 CA016042NCI NIH HHS R24 CA092865NCI NIH HHS U24 CA092865
6 · The paper itself

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.

Indexed as

Models, AnatomicSubtraction TechniqueAnimalsArtificial IntelligenceComputer SimulationFemaleImaging, Three-DimensionalMaleMiceMice, Inbred C57BLPattern Recognition, AutomatedRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsSensitivity and SpecificityTomography, X-Ray Computed

Identifiers

PMID23542374
PMCPMC3667217

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.