Evidence map›Paper›PMID 25283347›Full record

ArticleMicrovascular research2015

Segmentation and quantification of blood vessels for OCT-based micro-angiograms using hybrid shape/intensity compounding.

Siavash Yousefi, Ting Liu, Ruikang K Wang

Abstract read
In one paragraph

Article in Microvascular research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
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

27 citing papers in PubMed.

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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

3 authors.

Siavash YousefiDepartment of Bioengineering, University of Washington, Seattle, WA 98195, USA.
Ting LiuDepartment of Bioengineering, University of Washington, Seattle, WA 98195, USA; Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
Ruikang K WangDepartment of Bioengineering, University of Washington, Seattle, WA 98195, USA. Electronic address: wangrk@uw.edu.

Funding

OPTICAL MICROANGIOGRAHY OF CEREBROVASCULAR PERFUSIONR01HL093140 · NHLBI · UNIVERSITY OF WASHINGTON · PI WANG, RUIKANG · 2008 to 2017
$4.2M
Volumetric imaging of blood perfusion and tissue morphology in the cochleaR01DC010201 · NIDCD · UNIVERSITY OF WASHINGTON · PI WANG, RUIKANG · 2009 to 2013
$2.2M
Label-free optical imaging of 3D structural and functional microcirculationsR01EB009682 · NIBIB · UNIVERSITY OF WASHINGTON · PI WANG, RUIKANG · 2009 to 2012
$1.7M
NON-INVASIVE REAL-TIME LABEL-FREE 3D IMAGING OF RETINAL MICROCIRCULATIONR01EY024158 · NEI · UNIVERSITY OF WASHINGTON · PI WANG, RUIKANG · 2014 to 2017
$1.7M
NEI NIH HHS R01 EY024158NEI NIH HHS R01EY024158NHLBI NIH HHS R01 HL093140NHLBI NIH HHS R01HL093140NIBIB NIH HHS R01 EB009682NIBIB NIH HHS R01EB009682NIDCD NIH HHS R01 DC010201NIDCD NIH HHS R01DC010201
6 · The paper itself

Abstract

Optical coherence tomography (OCT) based microangiography is capable of visualizing 3D functional blood vessel networks within microcirculatory tissue beds in vivo. To provide the quantitative information of vasculature from the microangiograms such as vessel diameter and morphology, it is necessary to develop efficient vessel segmentation algorithms. In this paper, we propose to develop a hybrid Hessian/intensity based method to segment and quantify shape and diameter of the blood vessels innervating capillary beds that are imaged by functional OCT in vivo. The proposed method utilizes multi-scale Hessian filters to segment tubular structures such as blood vessels, but compounded by the intensity-based segmentation method to mitigate the limitations of Hessian filters' sensitivity to the selection of scale parameters. Such compounding segmentation scheme takes advantage of the morphological nature of Hessian filters while correcting for the scale parameter selection by intensity-based segmentation. The proposed algorithm is tested on a wound healing model and its performance of segmentation vessels is quantified by a publicly available manual segmentation dataset. We believe that this method will play an important role in the quantification of micro-angiograms for microcirculation research in ophthalmology and diagnosing retinal eye diseases involved with microcirculation.

Indexed as

Wound HealingAlgorithmsAnimalsArtifactsImage Interpretation, Computer-AssistedImaging, Three-DimensionalMaleMice, HairlessMicrocirculationMicrovesselsModels, AnimalNeovascularization, PhysiologicPattern Recognition, AutomatedPredictive Value of TestsSkinTime FactorsHessian filtersIntensity-based segmentationOptical coherence tomographyOptical microangiographyVessel network

Identifiers

PMID25283347
PMCPMC4275398

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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.