Multimodal and multimedia image analysis and collaborative networking for digestive endoscopy

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Multimodal and multimedia image analysis and

collaborative networking for digestive endoscopy

L D'orazio, A. Bartoli, A. Baetz, S. Beorchia, G. Calvary, Y. Chabane,

F. Chadebecq, T. Collins, Y. Laurillau, L. Martins-Baltar, et al.

To cite this version:

L D'orazio, A. Bartoli, A. Baetz, S. Beorchia, G. Calvary, et al.. Multimodal and multimedia

image analysis and collaborative networking for digestive endoscopy. Innovation and Research in

BioMedical engineering, Elsevier Masson, 2014, 35 (2), pp.88-93. �10.1016/j.irbm.2014.02.006�.

�hal-02061333�

ANR

TECSAN

2010

Multimodal

and

multimedia

image

analysis

and

collaborative

networking

for

digestive

endoscopy

L.

d’Orazio

,

A.

Bartoli

,

A.

Baetz

,

S.

Beorchia

,

G.

Calvary

,

Y.

Chabane

,

F.

Chadebecq

,

T.

Collins

,

Y.

Laurillau

,

L.

Martins-Baltar

,

B.

Mohamad

,

T.

Ponchon

,

C.

Rey

,

C.

Tilmant

,

S.

Torti

a_{InstitutPascalUMR6602,universitéBlaise-Pascal,CNRS,LIMOSUMR6158,campusdesCézeaux,BP125,63173Aubière,France} b_{Universitéd’Auvergne,CNRS,ISITUMR6284,France}

c_{Yansys,France}

d_{GrenobleINP,universitéPierreMendes-France,CNRS,LIGUMR5217,France} e_{HôpitalÉdouard-Herriot,France}

Abstract

Objective.–TheultimategoaloftheSyseoprojectistocreateachainofcollaborativeprocessestoallowthehepato-gastroenterologyendoscopy specialisttomanageimageseasily.

Methods.–Afieldstudyhasbeendonetobetterunderstandandformalizepracticesandcontextsofuse.Basedontheseresults,wehavedesigned toolsforgastroenterology,tacklingseveraldomainsofcomputerscienceandreusingwell-knownformatorconceptsespeciallyDICOMfiles, semanticretrievalandinfocus-breakpoint.

Results.–Syseoconsistsinfourmaincomponents:(1)adatamanagementsystemrelyingonthewell-knownstandardDICOMformat;(2)apolyp ontologyanddescriptionlogicstomanagegastroenterologicalimages;(3)softwaretoestimatethesizeofaneoplasiafromcolonoscopicimages and(4)pearlyuserinterfacestoenhancecollaboration.

Discussion.–PreliminaryresultsofSyseoarequitepromisingsincetheproposedsolutionsenabletoefficientlystore,annotate,retrievemedical data,whileprovidingrelativelyaccuratemeasuringtoolsforphysiciansandmedicalstaff.

©2014PublishedbyElsevierMassonSAS.

1. Introduction

Up until recently the specialities, whichclaim to be from

medical imaging resulted mainly from radiology techniques

shelves.Thereby,forthelast10years,thefocuswasessentially

on the settingup of interpretingand storage unitssoftware’s

stationsforheavyequipment(MRI,SCANNERS).Thevalueof

thesesolutionsisrelatedtothehighpriceoftheconnected

appli-ancesandthegeneratedgainsbysavingonfilms.Thisstrategy

oftherolloutpredominantlycentredonpublichospitalsended

uppenetratingsharplytheprivatecompaniesinreferencetothe

governmentincentivestoarchivethepatients’radiologicalfilms

andtoswaptodigitalonly.Thismeasureisstillrelevanttoday

anditbringsarealworkcomfortfortheradiologictechnicians,

andultimatelyfortheradiologisthimself.

The non-radiological images and video and the use of

thesearemoregruellinginsomespecialitiessuchasdigestive

endoscopy wherethe imageisacquired, interpretedandused

directlybythedoctorinrealtimeandwithoutthehelpof

manip-ulation.This“realtime”operatingmodemakestoughthesetting

upoftoolsforacquisitionorimagesscattering:aglobalsolution,

allinone,onlycouldallowtheiremergenceinoperatingtheatre.

The Syseoproject aims to createa chain of collaborative

processestoallowthehepato-gastroenterologyendoscopy

spe-cialist toproduce new images,archive, annotate andretrieve

themeasily,providinginadditioncomputervisiontools,

espe-cially toestimatemeasures.Thesoftwareergonomicsandthe

use ofnewdevices musttakeintoaccount thecontextof use

in ordertoextend, on onehand,thespan of an examination,

2. Methodandmaterial

The Syseo project aims to create a dynamic workflow

to enable endoscopy specialists, particularly in

hepato-gastroenterology, to easily manage images. In partnership

withgastroenterologists,medicalpractitionersandmedicalIT

experts,wedefinedatwo-foldapproachtodrivethiswork.The

firststepconsistedofafieldstudytobetterunderstandand

for-malizemedical practicesas well ascontexts ofuse of health

careapplications.Itleadsustoproduceformalrepresentations

ofmedicalpracticesandcontextsofuse.Basedontheseresults,

thesecondstepconsistedindesigningtoolsforgastroenterology.

Stillinpartnershipwithgastroenterologists,thisstepleadsusto

identifynewneedsandrequirements.

Inordertounderstandmedicalpracticesingastroenterology

andtoidentify the different contextsof userinteraction with

healthcareinteractivesystems,athoroughstudyofthefieldhas

beenconductedinthreephases:

• meetingswithdoctorsandsecretaries;

• analysisandmodellingoftheirneeds;

• validationofthisworkwithdifferentactors.

The restof this section introduces the backgroundof this

work, namely the DICOM standard, semantic retrieval and

infocus-breakpoint.

2.1. DICOMstandard

DICOM standard (http://medical.nema.org) aims to make

it possible interoperability between medical imaging

sys-tems, especiallyto make it easier exchange of medical data.

DICOMfile hasa hybridstructure that contains regulardata

(patient/clinicalinformation),multimediadata(images,3D

vol-umes,video,waveform,graphics),andservices(store,print).

DatainsideaDICOMfileisformedasagroupofattributes.

Eachattributeisdefinedbyalabelandthelengthofitscontent.

Alabelisrepresentedbytwohexadecimalnumbers:thefirstis

thegroupnumber(0010forpatientgroup)andthesecondisthe

elementnumber(0020forpatientid).

2.2. Semanticimagemanagement

Thesemanticapproachisbasedonanontologyofthedomain

[1],thatisadictionarywherewords(referredasconcepts)are

givenadefinition,expressedwithotherconceptshaving

them-selves adefinition, andso on.Our ontology thusmodels the

colorectalpolypsdomain,asafirststeptowardstheentire

gas-troenterologyfield.Itsconceptsareusedaskeywordstoannotate

polypimagesandtoexpressimagequeries.Threemainkinds

ofannotationsareavailable:imageobservations,polyp

classi-ficationsandsuspecteddiseases.Toannotatetheirimagesand

toexpresstheirimagequeries,practitionersnavigatethrougha

treeinterfacetochoosetherightconcepts.Thisinterfaceis

intu-itiveshowingonlytheconceptnamesandnottheirdefinitions.

Sinceitisbasedonthesedefinitions,theunderlyingautomated

retrievalprocess issaid tobe semantic. It consists of logical

inferencetechniquescalledreasoningswhichmakeitflexible,

sincerelevantimagescanberetrievedevenifnotannotatedwith

thesameconceptsasthequeryones.

2.3. Usingblurtoestimatedepth

Syseoexploitsimageblurtoestimateaneoplasia’ssize.A

detailedpresentationofoursystemcanberetrievedin[2].This

usestwokeyrelationships.Thefirstone,R1,existsbetweenthe

distanced fromthecolonoscopetothe neoplasia,the

neopla-sia’ssizeintheimage(inpixels)andtheneoplasia’srealsizes

(inmm).Thesecondone,R2,existsbetweenthelevelofimage

blurb(alsocalled‘defocus’orsimply‘blur’)andd.

BothR1andR2arewellknowninthecomputervision

lit-erature.R1maybeeasilyunderstoodfromasimplegeometric

reasoning:as aconsequence ofthelaw ofperspective

projec-tion,the closertothe colonoscopeafixedsize neoplasia, the

largeritsimage.R2ontheotherhandisanaturalconsequence

ofthecolonoscopefocusingatafocusingplanelyingata

pre-defineddistancef.Theneoplasia’simageissharpford=fand

isblurredotherwise.IthasbeenshownthatR2canbemodeled

usinggeometricaloptics.

Usingblurtoestimated isnotnew. Thiswas exploitedin

theso-calledshape-from-focusandshape-from-defocus

meth-ods[3].Theformerextractsthesharpestimagefromanimage

sequencewithvaryingimagingparameters.Thelatterusestwo

ormoreimageswithdifferentopticalsettingstoinferd.Both

methods tend to be unstable in colonoscopy for the

colono-scope’simagestendtobefocusedforawiderangeofdistances:

finding the sharpestimage(for shape-from-focus)or the blur

discrepancy (for shape-from-defocus) is thus unstable.Syseo

resolves this problem by introducing a singular point in the

blur-to-depthrelationship:theinfocus-breakpoint.

3. Results

Fig.1,overviewoftheSyseosystem,givesanoverviewof

theglobalsystemconsistinginfourmaincomponents:

• adatamanagementsystemwhichmakesitpossibletoproduce

andstorehighdefinition imagesandvideosintheDICOM

formattobemassivelystorerelyingonthecloudcomputing

paradigm;

• apolypontologytoannotatethesedocumentsandprovidean

efficientretrievalprocess;

• postprocessingtools toenabletomeasurethesize of

neo-plasiasfromimages;

• inorderforthesystemtobeusedinseveralcontexts,in

sev-eralplaces(anhospital,aphysicianofficeoranamphitheatre

during alectureor aconference)andby differentof users

(physicians,nurses,students,etc.),usingplasticitydesignand

enhancingcollaborationwithpearlyuserinterfaces.

3.1. Datamanagementsystem

Weproposeahybrid(row-column)twolayersdatastorage

Fig.1.OverviewoftheSyseosystem.

• tomanagehighdegreeofheterogeneityofDICOMfiles;

• tostorelargeamountofdata;

• toenabledatatoevolvetomatchnewversionsofthestandard.

Both(row-column)layersarecloud-based,whichensuresthe

elasticityandfaulttoleranceforeachofthem.Another

impor-tantaspectisagoodlevelofnormalizationofdataforeach

layertoreducethestoragecost.

We propose to store mandatory/frequently used attributes

andthefrequentlytogetherattributesinarow-orienteddatabase

layertoimprovethequeryexecutiontime,byminimizingthe

tuplereconstructiontime.Theadvantageofthislayerisits

write-optimizedfeature(eachtupleinsertioninrow-orienteddatabases

needsonediskblockI/Oforinsertionalone).Thus,havingalot

ofinsertsoverthislayerwillnotbechallenging.

Optional/privateattributesvaryenormouslyfromone

medi-calfiletoanotherandfromonemedicalcentertoanother.To

manage heterogeneity, we propose storing them in

column-oriented databases. Only non-null attributes values will be

insertedintotheircorrespondingcolumns.Thislayeroffersthe

ability to perform efficiently ad-hoc/statistical queries.

Addi-tionally,thephysicalstructureofthesesystemsprovideagood

solution for theevolving schemaissue,sinceeach column is

storedinaseparatediskblock.

3.2. Polypontology

Grounded on the ontology and the associated annotation

interfacepresentedinsection2.2,thesemanticsearchofpolyp

imagesisachievedbythreereasonings.Theseareinference

tech-niquesbased onthe description logicsformalism [4] andthe

OWLwebontologylanguage(http://www.w3.org/2007/OWL).

Thefirst(R1)istheclassicalindividualretrievalreasoning(here

individualsarepolypimages):givenapolypclassification(resp.

animagequery),itfindsalltheimageswhichannotation

log-ically implies theclassification (resp.the query).The second

reasoning (R2) is the exact classes retrieval: givenan image

annotation,andthenameofaclassification,theissueistofind

theexactsubclassesthisannotationbelongsto,i.e.thesubclasses

whichall definitionpropertiescanbe inferredfrom theinput

imageannotation.Thethirdreasoning(R3)istheapproximated

classesretrieval:givenanimageannotation,andthenameofa

classification,theissueistofind theapproximatedsubclasses

thisannotationbelongs to,i.e.thesubclasses fromthe

defini-tionofwhichwecaninferallthepropertiesoftheinputimage

annotation.Aninterestingpointwiththepreviousreasoningsis

theircomposability.Forexample,combiningS1afterS2allows

toretrieveallthereferenceimageannotationsthatbelongtothe

exactclassesofaninputimageannotation.

3.3. Theinfocus-breakpoint

TheInfocus-Breakpointcorrespondstotheshortest

colono-scopetoneoplasiadistanceontheedgeofthefocusingrange.

Thisisusuallyaquiteshortdistance,oftheorderofafew

mil-limetres.Thisdistancecanbefirstprecalibrated,andisthenused

invivoforinteractiveneoplasiasizeestimationusingR1andR2,

asexplaineddirectlybelow.

Wehavedevelopedaninfocus-breakpointestimationmodule.

Givenashortvideo,thismoduletracksaregionandcomputes

itsinfocus-breakpointautomatically.Oursystemmaybeused

aftercalibrationofR1andR2hasbeencarriedout.

Thegoalofcalibrationistorecovertheparametersinvolved

inR1andR2.Calibrationiscarriedoutonlyonetimeand

pre-operatively. Itconsistsinmovingacalibrationapparatussuch

Thestructureof thecalibrationapparatus isknowntoagood

accuracy,andmakestwothingspossible:

• wecancalibratethecolonoscope’sgeometricproperties,this

givesR1;

• we canestimate the calibrationapparatus’s distance tothe

colonoscopeattheinfocus-breakpoint,thisgivesR2.

Intheintraoperativecourse,thegastroenterologistmanually

marksaneoplasiausingacomputer’smouse.Oursystemtracks

itwhilethecolonoscopeismovedaroundtheneoplasia,andfinds

theinfocus-Breakpoint,fromwhichR1andR2maybeexploited

onaselectedimage.Thisallowsthegastroenterologisttoobtain

asizemeasurementbyclickingononeofthisimage.

3.4. PearlyUI

Inordertofosterthecollaborationamongpractitionersandto

takebenefitfromtheknowledgeofdiseasesinfamilies,Syseo

promotessocialrelationships as keyforimproving care

qual-ity.It investigates the Pearls, a cloud-oriented user interface

metaphorthat embracesthekeycharacteristics ofcloud

com-puting:bigdata,on-demandservicesandtheconvergencewith

socialcomputing.

Ataconceptuallevel,themetaphorswitchesfromclassical

coreentities(data,actorandtask)tosociallyaugmentedentities

(SAE)enhancingtheirinterrelationships(data-actor,data-task

andtask-actor)inordertointegratethesocialdimensionintoa

service-orienteduserinterface.

Pearlsaremeansforrevealingtherelationshipsbetween

enti-ties. APearlisaheterogeneous collectionof actors, services

anddata.Itisanedgeofahypergraphwhoseverticesare

ele-mentsofasetthatisitselftheunionofthreesetsofcoreentities

(actors, services anddata). Such agraph represents apartial

viewof actors’ socialnetwork andthus highlightsthe social

workofactorentities,asthefictivesocio-professionalnetwork

ofapractitioner.

4. Discussion

TheSyseoprojecttacklesfourmainresearchdomains:data

storage, semantic annotation and retrieval, human computer

interactionandthesizeofneoplasias.Thissectionpresentssome

ofthemainworksrelatedtothesedomains.

4.1. Datastorage

Thewideuseofthisstandardinthemedicaldomainhasled

tothedevelopmentofsomeDICOMmanagementsystems:the

picture archivingandcommunication system(PACS)[5],the

mostwidelyused DICOMmanagementsystem,using mostly

relational databases tostore DICOMfiles; eDiaMoND[6], a

grid-enableddatabaseof mammogramimagesandthe

ORDI-COMdata typeinOracle11G [7] enabling tostoreDICOM

fileasanobjectinacolumnofadatabasetable.Unfortunately,

suchsystemsarehighlyexpensive,ITexperts-dependent,weak

expressivenessor/andnotscalable.Particularly,incurrent

sys-temsthecrashofaservermaypreventdoctorsfromgettingthe

requiredimageifitisnotstoredonaseparateportabledisk.

Duetothecharacteristicsofmedicaldataapplicationssuchas

theheterogeneity,theextremelyhuge/ever-increasingsize,and

theexpensivestorage,itwouldbebeneficialtoexploitthepower

ofcloud-basedsystems,likeMapReduce[8],oritsopensource

version Hadoop (http://hadoop.apache.org/), Amazon

Sim-pleDB,AmazonDynamoDB,AmazonRDS(aws.amazon.com),

SQL Azure (www.windowsazure.com/services/sql-database/),

Pig [9], Hive [10],SCOPE [11]or Jaql [12],to handlesuch

challenges. This isbecause thesesystems provide promising

solutionsof cost-effectiveness,disasterrecoverability,

elastic-ity,manageability,andavailability.Nevertheless,noneofthese

systemsconsiderthecomplexityoftheDICOMformat.

4.2. Semanticannotationandretrieval

Theuseofdescriptionlogicsreasoningtogrounda

seman-ticimageretrievalprocessisnotanewidea.Wecanfindtwo

classicalapproaches[13–16]whichcorrespondtoreasoningR1,

whichistheclassicalindividualsretrieval,andthecomposition

ofR2followedbyR1,whichamountstofindingimages

asso-ciatedtoconcepts that havethesamepropertiesas the query

(andmaybeothers).Otherapproachesarebasedonnon-standard

reasonings (abductionandcontraction) [17,18],whichimply,

however,tousealessexpressivelanguagethaninourapproach.

These reasoningsgeneralise the previous ones by enabling a

finer ranking of answers. Our workis situated betweenboth

approaches:basedonstandardreasoningswithahigh

expres-siveness language, we handle a fine ranking of answers by

allowingtheusertointeractwiththequeryinterface.

4.3. Computervision

Developingacomputer-aidedneoplasia’ssizemeasurement

software ispartof the fieldofcomputer vision,whose major

topicisstudyinghowworld-sizemeasurementsmaybeinferred

fromimages.

However, most monocular measurement systems such as

structure-from-motion provide relative measurements only,

unlessatleastonephysicalmeasurement,forinstanceaworld’s

lengthorthedistancebetweentwocamerapositions,canbe

pro-vided[19].Thisisnoteasilypossibleincolonoscopy:theseare

nophysicallengthgaugevisiblenorcanonetracktheflexible

colonoscope’stipposition.Inotherwords,thoughthose

tech-niquesmaygiveanestimateofaneoplasia’sshape,theywillnot

recoveritsabsolutesize.Intermsofpracticalities,colonoscopic

images alsoraise extremelyspecificissuescausedin

particu-lar bya lack of discriminativevisual landmarks andmoving

specularities(duetowettissues).

4.4. Humancomputerinteraction

Ongoing research on cloud-based UIs are data-centric

[20,21].Wepromoteanenlargedfoldermetaphortointegratethe

in terms of applications and services for health care. Many

worksproposedmodelsorframeworksbasedoncloud

comput-ingforimprovinghealthcareservices,forinstanceaframework

forcolorectalcancerimaginganalysis[22].Otherexamplesof

cloudbased systemsare proposedtoautomatetheprocessof

collectingpatients’data,toofferaubiquitousaccessofpatient

healthinformationortoenhancemobilehealthapplicationfor

societalservices[23].

5. Conclusion

ThisarticlepresentedtheSyseoprojectsanditscontributions:

• a hybrid database for the management of highly

hetero-geneous and voluminous medical data to provide ease of

use,extensibility,highperformanceandad-hocqueriesover

DICOMandtogetbenefitoftheelasticity,billingbyuseand

scalabilityofthecloud;

• a semantic image retrieval approach grounded on a polyp

ontologyexpressedintheOWLlanguageandthree

compos-ablereasonings;

• acost-neutral methodologyfor measuring the size of

neo-plasiasfromregularopticalimages,whichdoesnotrequire

specialengineeringofthecolonoscope;

• aservice-orienteduserinterfacemetaphortakingintoaccount

medicalaspectsaswellasuserinteractionaspectsand

rely-ingontheconcept ofsociallyaugmentedentitytoconsider

the social dimension of activities and on a formal

defini-tion,accompaniedbyaconceptualrepresentation,appliedto

gastroenterology.

The Syseo project opens research perspective in the four

domainstobeconsidered:datastorage,semanticannotationand

retrieval,computervisionandhumancomputerinteraction.

Thenextobjective,withrespecttodatastorage,istoachievea

highlevelofQoSthatallowsqueryinglargeamountsofdatavia

differenttypesof computingdevices.Thesecurityofmedical

dataoverthecloudcouldbeaninterestingfuturework.

Addition-ally,someoptimization(e.g.materializedviews,cachemanager)

shouldberethoughtforourparticularstructure.

To be effective, a semantic image retrieval process needs

to tightly couple ontology modeling, image annotation and

retrievalreasoning.Thisiswhat wepropose here:newpolyp

ontology,anintuitiveannotationinterfacebuiltfromthe

ontol-ogy and three composable reasonings to propose a flexible

retrieval.Asimagesarestoredinthecloud,aninteresting

per-spectivewillbetobenefitalsofromthecomputationcapacities

ofthecloudbymovingourreasoningcomputationthere[24,25].

Anotherperspectiveofthisworkistoimproveperformancesof

theretrieval reasoningbyusing anontologicalquery

answer-ingapproach,whichtranslatesthequeryandtheontologyina

classicalrelational database contextso as tobenefitfromthe

perfomances of the existing optimized database management

systems[26,27].

Regardingsizemeasurement, oursystem’smainlimitation

lies in R1, which makes the assumption that the neoplasia

ismostly paralleltothe colonoscope’sdistal endplane. This

assumptionisobviouslyviolatediftheneoplasia’sslantistoo

strong.Inpractice,thegastroenterologistsareinformedthatthey

mustkeepthecolonoscopeinthebestpossibleorientationwith

respecttotheneoplasiabeingmeasured,thoughraisingthis

con-straintwouldmakeoursystemmoreflexible.Oursystemmay

alsobefurtherimprovedbydecouplingopticsandmotionblur.

Early feedback shows that the Pearlmetaphor constitutes

an interesting means to addressthe sharing of medical data,

traceability, capitalizationof endoscopic images andthus the

improvementhealthcarequality.Asaperspective,weplanned

to develop andto evaluate afully running prototype as well

as investigatingtheadaptation oftheuserinterfacefor

differ-entcontextsofuse(e.g.operatingroomvs.medicalofficewith

variousinteractiondevices).

Disclosureofinterest

The authors declare that theyhaveno conflictsof interest

concerningthisarticle.

Acknowledgement

This work is in collaboration with the Yansys

Company/Syseo® project.ItissponsoredbyCRd’Auvergne,

SGAR Auvergne and the ANR under grant SYSEO

ANR-10-TECSAN-005-01.

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