Evidence map›Paper›PMID 30946667›Full record

ArticleIEEE transactions on image processing : a publication of the IEEE Signal Processing Society2019

Three-Dimensional Pose Estimation for Laboratory Mouse From Monocular Images.

Ghadi Salem, Jonathan Krynitsky, Monson Hayes, Thomas Pohida, Xavier Burgos-Artizzu

Abstract read
In one paragraph

Article in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2019. 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
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  4. Review
  5. Article
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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

5 authors.

Ghadi Salem
Jonathan Krynitsky
Monson Hayes
Thomas Pohida
Xavier Burgos-Artizzu

Funding

Signal Processing And Instrumentation SectionZIACT000261 · CIT · CENTER FOR INFORMATION TECHNOLOGY · PI POHIDA, THOMAS J · 2009 to 2021
$16.7M
Intramural NIH HHS Z99 CT999999
6 · The paper itself

Abstract

Video-based activity and behavior analysis of mice has garnered wide attention in biomedical research. Animal facilities hold large numbers of mice housed in "home-cages" densely stored within ventilated racks. Automated analysis of mice activity in their home-cages can provide a new set of sensitive measures for detecting abnormalities and time-resolved deviation from the baseline behavior. Large-scale monitoring in animal facilities requires minimal footprint hardware that integrates seamlessly with the ventilated racks. The compactness of hardware imposes the use of fisheye lenses positioned in close proximity to the cage. In this paper, we propose a systematic approach to accurately estimate the 3D pose of the mouse from single-monocular fisheye-distorted images. Our approach employs a novel adaptation of a structured forest algorithm. We benchmark our algorithm against existing methods. We demonstrate the utility of the pose estimates in predicting mouse behavior in a continuous video.

Indexed as

Behavior, AnimalAnimalsBiomedical ResearchHousing, AnimalImaging, Three-DimensionalMicePostureVideo Recording

Identifiers

PMID30946667
PMCPMC6677238

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.