Evidence map›Paper›PMID 28883434›Full record

ArticleScientific reports2017

Automated video-mosaicking approach for confocal microscopic imaging in vivo: an approach to address challenges in imaging living tissue and extend field of view.

Kivanc Kose, Mengran Gou, Oriol Yélamos, Miguel Cordova, Anthony M Rossi, Kishwer S Nehal, Eileen S Flores, Octavia Camps, Jennifer G Dy, Dana H Brooks and 1 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
7.4field-weighted citation impact, top 3% 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

23 citing papers in PubMed, 41 citations in OpenAlex.

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  10. Real-Time Nonrigid Mosaicking of Laparoscopy Images.IEEE transactions on medical imaging · 2021
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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

11 authors at 2 institutions in 2 countries.

Kivanc KoseDermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA. kosek@mskcc.org.ORCID 0000-0003-3185-2639
Mengran GouElectrical and Computer Engineering, Northeastern University, Boston, MA, USA.
Oriol YélamosDermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID 0000-0002-8058-3095
Miguel CordovaDermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Anthony M RossiDermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Kishwer S NehalDermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Eileen S FloresDermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Octavia CampsElectrical and Computer Engineering, Northeastern University, Boston, MA, USA.
Jennifer G DyElectrical and Computer Engineering, Northeastern University, Boston, MA, USA.
Dana H BrooksElectrical and Computer Engineering, Northeastern University, Boston, MA, USA.
Milind RajadhyakshaDermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Memorial Sloan Kettering Cancer Center · USNortheastern University · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
Automated Image Guidance for Diagnosing Skin Cancer With Confocal MicroscopyR01CA199673 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI DY, JENNIFER G, RAJADHYAKSHA, MILIND · 2015 to 2018
$2.5M
Intra-operative confocal imaging to guide Mohs surgeryR01EB012466 · NIBIB · SLOAN-KETTERING INST CAN RESEARCH · PI RAJADHYAKSHA, MILIND · 2010 to 2012
$2.3M
Peri-operative confocal imaging-guided laser ablation of basal cell carcinomasR01EB020029 · NIBIB · SLOAN-KETTERING INST CAN RESEARCH · PI RAJADHYAKSHA, MILIND · 2015 to 2018
$2.2M
Fiber bundle-based divided-pupil confocal endoscopeR01CA156773 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI RAJADHYAKSHA, MILIND · 2010 to 2012
$1.8M
NCI NIH HHS P30 CA008748NCI NIH HHS R01 CA156773NCI NIH HHS R01 CA199673NIBIB NIH HHS R01 EB012466NIBIB NIH HHS R01 EB020029
6 · The paper itself

Abstract

We describe a computer vision-based mosaicking method for in vivo videos of reflectance confocal microscopy (RCM). RCM is a microscopic imaging technique, which enables the users to rapidly examine tissue in vivo. Providing resolution at cellular-level morphology, RCM imaging combined with mosaicking has shown to be highly sensitive and specific for non-invasively guiding skin cancer diagnosis. However, current RCM mosaicking techniques with existing microscopes have been limited to two-dimensional sequences of individual still images, acquired in a highly controlled manner, and along a specific predefined raster path, covering a limited area. The recent advent of smaller handheld microscopes is enabling acquisition of videos, acquired in a relatively uncontrolled manner and along an ad-hoc arbitrarily free-form, non-rastered path. Mosaicking of video-images (video-mosaicking) is necessary to display large areas of tissue. Our video-mosaicking methods addresses this need. The method can handle unique challenges encountered during video capture such as motion blur artifacts due to rapid motion of the microscope over the imaged area, warping in frames due to changes in contact angle and varying resolution with depth. We present test examples of video-mosaics of melanoma and non-melanoma skin cancers, to demonstrate potential clinical utility.

Indexed as

HumansImage Processing, Computer-AssistedMelanomaMicroscopy, ConfocalMicroscopy, VideoSkin Neoplasms

Identifiers

PMID28883434
PMCPMC5589933
OpenAlexW2752043212

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.