Evidence map›Paper›PMID 28386607›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2016

Automatic Cystocele Severity Grading in Ultrasound by Spatio-Temporal Regression.

Dong Ni, Xing Ji, Yaozong Gao, Jie-Zhi Cheng, Huifang Wang, Jing Qin, Baiying Lei, Tianfu Wang, Guorong Wu, Dinggang Shen

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

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 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
2.3field-weighted citation impact, top 6% 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, 4 citations in OpenAlex.

  1. Observational
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 4 institutions in 3 countries.

Dong NiNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
Xing JiNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
Yaozong GaoDepartment of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC 27599, USA.
Jie-Zhi ChengNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
Huifang WangDepartment of Ultrasound, Shenzhen Second Peoples Hospital, Shenzhen, China.
Jing QinSchool of Nursing, Centre for Smart Health, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
Baiying LeiNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
Tianfu WangNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
Guorong WuDepartment of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC 27599, USA.
Dinggang ShenDepartment of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC 27599, USA.
Shenzhen University · CNUniversity of North Carolina Health Care · USHong Kong Polytechnic University · HKShenzhen Second People's Hospital · CN

Funding

Quantifying Brain Abnormality by Multimodality Neuroimage Analysis,R01AG041721 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIU, MINGXIA · 2012 to 2019
$3.0M
Longitudinal Analysis for Early Diagnosis of Alzheimer's DiseaseK01AG049089 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI WU, GUORONG · 2016 to 2019
$478k
NIA NIH HHS K01 AG049089NIA NIH HHS R01 AG041721
6 · The paper itself

Abstract

Cystocele is a common disease in woman. Accurate assessment of cystocele severity is very important for treatment options. The transperineal ultrasound (US) has recently emerged as an alternative tool for cystocele grading. The cystocele severity is usually evaluated with the manual measurement of the maximal descent of the bladder (MDB) relative to the symphysis pubis (SP) during Valsalva maneuver. However, this process is time-consuming and operator-dependent. In this study, we propose an automatic scheme for csystocele grading from transperineal US video. A two-layer spatio-temporal regression model is proposed to identify the middle axis and lower tip of the SP, and segment the bladder, which are essential tasks for the measurement of the MDB. Both appearance and context features are extracted in the spatio-temporal domain to help the anatomy detection. Experimental results on 85 transperineal US videos show that our method significantly outperforms the state-of-the-art regression method.

Indexed as

AdultAlgorithmsCystoceleFemaleHumansRegression AnalysisReproducibility of ResultsSensitivity and SpecificitySeverity of Illness IndexUltrasonographyYoung AdultCystoceleRegressionSpatio-temporalUltrasound

Identifiers

PMID28386607
PMCPMC5380365
OpenAlexW2524317021

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.