Repository logo
 

Deep learning applied to hyperspectral endoscopy for online spectral classification.

Simple item page
cam.issuedOnline2020-03-03
cam.orpheus.successMon Jun 08 08:23:42 BST 2020 - The item has an open VoR version.
dc.contributor.authorGrigoroiu, Alexandru
dc.contributor.authorYoon, Jonghee
dc.contributor.authorBohndiek, Sarah E
dc.contributor.orcidBohndiek, Sarah E [0000-0003-0371-8635]
dc.date.accessioned2020-02-14T00:31:58Z
dc.date.available2020-02-14T00:31:58Z
dc.date.issued2020-03-03
dc.description.abstractHyperspectral imaging (HSI) is being explored in endoscopy as a tool to extract biochemical information that may improve contrast for early cancer detection in the gastrointestinal tract. Motion artefacts during medical endoscopy have traditionally limited HSI application, however, recent developments in the field have led to real-time HSI deployments. Unfortunately, traditional HSI analysis methods remain unable to rapidly process the volume of hyperspectral data in order to provide real-time feedback to the operator. Here, a convolutional neural network (CNN) is proposed to enable online classification of data obtained during HSI endoscopy. A five-layered CNN was trained and fine-tuned on a dataset of 300 hyperspectral endoscopy images acquired from a planar Macbeth ColorChecker chart and was able to distinguish between its 18 constituent colors with an average accuracy of 94.3% achieved at 8.8 fps. Performance was then tested on a set of images simulating an endoscopy environment, consisting of color charts warped inside a rigid tube mimicking a lumen. The algorithm proved robust to such variations, with classification accuracies over 90% being obtained despite the variations, with an average drop in accuracy of 2.4% being registered at the points of longest working distance and most inclination. For further validation of the color-based classification system, ex vivo videos of a methylene blue dyed pig esophagus and images of different disease stages in the human esophagus were analyzed, showing spatially distinct color classifications. These results suggest that the CNN has potential to provide color-based classification during real-time HSI in endoscopy.
dc.description.sponsorshipThe work was funded by the UK Engineering and Physical Sciences Research Council (EPSRC) (EP/N014588/1, EP/R003599/1), CRUK (C47594/A16267, C14303/A17197, C47594/A21102) and the EU FP7 agreement FP7-PEOPLE-2013-CIG-630729. AG was supported by the EPSRC grant for the University of Cambridge Centre for Doctoral Training in Sensor Technologies and Applications (EP/L015889/1). We would like to thank Dr di Pietro, Dr Januszewicz and Prof. Fitzgerald of the MRC Cancer Unit in Cambridge for their assistance in the original experiments that led to the generation of the pig esophagus and tissue biopsy datasets.
dc.format.mediumElectronic
dc.identifier.doi10.17863/CAM.49246
dc.identifier.eissn2045-2322
dc.identifier.issn2045-2322
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/302170
dc.languageeng
dc.language.isoeng
dc.publisherSpringer Science and Business Media LLC
dc.publisher.urlhttp://dx.doi.org/10.1038/s41598-020-60574-6
dc.rightsAll rights reserved
dc.subject4605 Data Management and Data Science
dc.subject46 Information and Computing Sciences
dc.subject4603 Computer Vision and Multimedia Computation
dc.subjectClinical Research
dc.titleDeep learning applied to hyperspectral endoscopy for online spectral classification.
dc.typeArticle
dcterms.dateAccepted2020-02-12
prism.issueIdentifier1
prism.publicationDate2020
prism.publicationNameSci Rep
prism.startingPage3947
prism.volume10
pubs.funder-project-idEuropean Commission (630729)
pubs.funder-project-idCancer Research Uk (None)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/N014588/1)
pubs.funder-project-idCancer Research UK (C14303/A17197)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/R003599/1)
pubs.funder-project-idEPSRC (1783080)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L015889/1)
pubs.funder-project-idCancer Research UK (21102)
rioxxterms.licenseref.startdate2020-03-03
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
rioxxterms.versionAM
rioxxterms.versionofrecord10.1038/s41598-020-60574-6

Files

Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
Deep_learning_applied_to_hyperspectral_endoscopy_for_online_spectral_classification (2).pdf
Size:
11.77 MB
Format:
Adobe Portable Document Format
Description:
Accepted version
Licence
http://www.rioxx.net/licenses/all-rights-reserved
Download
Thumbnail Image
Name:
s41598-020-60574-6.pdf
Size:
1.62 MB
Format:
Adobe Portable Document Format
Description:
Published version
Licence
http://www.rioxx.net/licenses/all-rights-reserved
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
DepositLicenceAgreementv2.1.pdf
Size:
150.9 KB
Format:
Adobe Portable Document Format
Download

Collections

Cambridge University Research Outputs