Evidence map›Paper›PMID 25525552›Full record

ArticleApplied mathematics2014

Automated Cell Detection and Morphometry on Growth Plate Images of Mouse Bone.

Maria-Grazia Ascenzi, Xia Du, James I Harding, Emily N Beylerian, Brian M de Silva, Ben J Gross, Hannah K Kastein, Weiguang Wang, Karen M Lyons, Hayden Schaeffer

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Applied mathematics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 16% of its field
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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

10 authors at 2 institutions in 1 country.

Maria-Grazia AscenziDepartment of Orthopaedic Surgery, University of California, Los Angeles, California 90095, USA.
Xia DuDepartment of Orthopaedic Surgery, University of California, Los Angeles, California 90095, USA.
James I HardingDepartment of Orthopaedic Surgery, University of California, Los Angeles, California 90095, USA.
Emily N BeylerianDepartment of Mathematics, University of California, Los Angeles, California 90095, USA.
Brian M de SilvaDepartment of Mathematics, University of California, Los Angeles, California 90095, USA.
Ben J GrossDepartment of Mathematics, University of California, Los Angeles, California 90095, USA.
Hannah K KasteinDepartment of Mathematics, University of California, Los Angeles, California 90095, USA.
Weiguang WangDepartment of Orthopaedic Surgery, University of California, Los Angeles, California 90095, USA.
Karen M LyonsDepartment of Orthopaedic Surgery, University of California, Los Angeles, California 90095, USA.
Hayden SchaefferDepartment of Mathematics, University of California, Irvine, California 92697, USA.
University of California, Los Angeles · USUniversity of California, Irvine · US

Funding

BMPS IN SKELETAL GROWTH AND OSTEOGENIC DIFFERENTIATIONR01AR044528 · NIAMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LYONS, KAREN M. · 1998 to 2017
$5.2M
NIAMS NIH HHS R01 AR044528
6 · The paper itself

Abstract

Microscopy imaging of mouse growth plates is extensively used in biology to understand the effect of specific molecules on various stages of normal bone development and on bone disease. Until now, such image analysis has been conducted by manual detection. In fact, when existing automated detection techniques were applied, morphological variations across the growth plate and heterogeneity of image background color, including the faint presence of cells (chondrocytes) located deeper in tissue away from the image's plane of focus, and lack of cell-specific features, interfered with identification of cell. We propose the first method of automated detection and morphometry applicable to images of cells in the growth plate of long bone. Through ad hoc sequential application of the Retinex method, anisotropic diffusion and thresholding, our new cell detection algorithm (CDA) addresses these challenges on bright-field microscopy images of mouse growth plates. Five parameters, chosen by the user in respect of image characteristics, regulate our CDA. Our results demonstrate effectiveness of the proposed numerical method relative to manual methods. Our CDA confirms previously established results regarding chondrocytes' number, area, orientation, height and shape of normal growth plates. Our CDA also confirms differences previously found between the genetic mutated mouse

Indexed as

Anisotropic diffusioncell detectiongrowth platemouseRetinex

Identifiers

PMID25525552
PMCPMC4267696
OpenAlexW2063259481

What OpenQuestion holds

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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.