Evidence map›Paper›PMID 32832764›Full record

ArticleACS omega2020

Target Area Extraction Algorithm for the In Vivo Fluorescence Imaging of Small Animals.

Qiang Zhang, Lei Wang, Qing Qian, Jishuai Wang, Wenbo Cheng, Kun Han

Open access · goldAbstract read
In one paragraph

Article in ACS omega, 2020. 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
–field-weighted citation impact, top 94% 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, 1 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

6 authors at 1 institution in 1 country.

Qiang ZhangAcademy for Engineering & Technology, Fudan University, Shanghai 200433, P. R. China.
Lei WangCAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, P. R. China.
Qing QianCAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, P. R. China.
Jishuai WangCAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, P. R. China.
Wenbo ChengCAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, P. R. China.
Kun HanCAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, P. R. China.
Chinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bio-optical imaging can noninvasively describe specific biochemical reaction events in small animals using endogenous or exogenous imaging reagents to label cells, proteins, or DNA. The fluorescence optical bio-imaging system excites the fluorescent group to a high energy state by excitation light and then generates emission light. However, many substances in the organism will also emit fluorescence after being excited by the excitation light, and the nonspecific fluorescence generated will affect the detection sensitivity. This paper designs and develops a set of high-level biosafety in vivo fluorescence imaging system for small animals suitable for virology research and proposes a target area extraction algorithm for fluorescence images. The fluorescence image target extraction algorithm first maps the nonlinear separation data in the low-dimensional space to the high-dimensional space. Then, based on the analysis of the characteristics of the fluorescent region, a method for discriminating the target fluorescent region based on the two-step entropy function is proposed, and the real target fluorescent region is obtained according to the set connected region. Based on the experiment of collecting and analyzing the in vivo fluorescent images of mice, it is verified that the proposed algorithm can automatically extract the target fluorescent region better than the classical linear model. It shows that the proposed algorithm is less affected by background fluorescence, and the estimated separated spectrum based on this method is closer to the real target spectrum.

Identifiers

PMID32832764
PMCPMC7439258
OpenAlexW3047113777

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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.