Evidence map›Paper›PMID 26392183›Full record

ArticleMedical & biological engineering & computing2016

A hybrid registration-based method for whole-body micro-CT mice images.

Xiaochao Qu, Xueyuan Gao, Xianhui Xu, Shouping Zhu, Jimin Liang

Abstract read
PubMed Publisher
In one paragraph

Article in Medical & biological engineering & computing, 2016. 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
0.3field-weighted citation impact, top 36% 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

5 authors at 1 institution in 1 country.

Xiaochao QuSchool of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China. Xiaochaoqu@gmail.com.
Xueyuan GaoSchool of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China.
Xianhui XuSchool of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China.
Shouping ZhuSchool of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China.
Jimin LiangSchool of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China.
Xidian University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The widespread use of whole-body small animal in vivo imaging in preclinical research has proposed the new demands on imaging processing and analysis. Micro-CT provides detailed anatomical structural information for continuous detection and different individual comparison, but the body deformation happened during different data acquisition needs sophisticated registration. In this paper, we propose a hybrid method for registering micro-CT mice images, which combines the strengths of point-based and intensity-based registration methods. Point-based non-rigid method using thin-plate spline robust point matching algorithm is utilized to acquire a coarse registration. And then intensity-based non-rigid method using normalized mutual information, Halton sampling and adaptive stochastic gradient descent optimization is used to acquire precise registration. Two accuracy metrics, Dice coefficient and average surface distance are used to do the quantitative evaluation. With the intra- and intersubject micro-CT mice images registration assessment, the hybrid method has been proven capable of excellent performance on micro-CT mice images registration.

Indexed as

AnimalsImage Processing, Computer-AssistedMiceWhole Body ImagingX-Ray MicrotomographyHybrid registrationMicro-CT mice imagesNon-rigid registration

Identifiers

PMID26392183
OpenAlexW2233271813

What OpenQuestion holds

Textmetadata
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.