Evidence map›Paper›PMID 33015187›Full record

ArticleBioMed research international2020

Segmentation of Cerebrovascular Anatomy from TOF-MRA Using Length-Strained Enhancement and Random Walker.

Ruoxiu Xiao, Cheng Chen, Hanying Zou, Ying Luo, Jiayu Wang, Muxi Zha, Ming-An Yu

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

Article in BioMed research international, 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
0.3field-weighted citation impact, top 43% 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, 5 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

7 authors at 2 institutions in 1 country.

Ruoxiu XiaoSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.ORCID https://orcid.org/0000-0002-2721-4813
Cheng ChenSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Hanying ZouSchool of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Ying LuoSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Jiayu WangSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Muxi ZhaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Ming-An YuDepartment of Interventional Ultrasound, China-Japan Friendship Hospital, Beijing 100029, China.ORCID https://orcid.org/0000-0003-0797-4564
University of Science and Technology Beijing · CNChina-Japan Friendship Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cerebrovascular rupture can cause a severe stroke. Three-dimensional time-of-flight (TOF) magnetic resonance angiography (MRA) is a common method of obtaining vascular information. This work proposes a fully automated segmentation method for extracting the vascular anatomy from TOF-MRA. The steps of the method are as follows. First, the brain is extracted on the basis of regional growth and path planning. Next, the brain's highlighted connected area is explored to obtain seed point information, and the Hessian matrix is used to enhance the contrast of image. Finally, a random walker combined with seed points and enhanced images is used to complete vascular anatomy segmentation. The method is tested using 12 sets of data and compared with two traditional vascular segmentation methods. Results show that the described method obtains an average Dice coefficient of 90.68%, and better results were obtained in comparison with the traditional methods.

Indexed as

AlgorithmsImage Processing, Computer-AssistedMagnetic Resonance AngiographyBrainHumansTime Factors

Identifiers

PMID33015187
PMCPMC7525292
OpenAlexW3088184409

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.