mvc open pdf in browser : How to extract a picture from a pdf Library SDK component .net wpf windows mvc article_diagnostic_pathology0-part770

Analyzing huge pathology images with open source software
ChristopheDeroulers
1
,DavidAmeisen
2
,MathildeBadoual
1
,ChloeGerin
3;4
,AlexandreGranier
5
and Marc Lartaud
5;6
1UnivParisDiderot,LaboratoireIMNC,UMR8165CNRS,UnivParis-Sud,F-91405Orsay,France
2
UnivParisDiderot,Laboratoiredepathologie,H^opitalSaint-LouisAPHP,INSERMUMR-S728,F-75010Paris,France
3
onleavefromCNRS,UMR8165,LaboratoireIMNC,UnivParis-Sud,UnivParisDiderot,F-91405Orsay,France
4nowatCNRS,UMR8148,LaboratoireIDES,UnivParis-Sud,F-91405Orsay,FranceandCNRS,UMR8608,IPN,UnivParis-Sud,
F-91405Orsay,France
5
MRI-MontpellierRIOImaging,CRBM,F-34293Montpellier,France
6
CIRAD,F-34398MontpellierCEDEX 5,France
Email: ChristopheDeroulers-deroulers@imnc.in2p3.fr;DavidAmeisen-david.ameisen@gmail.com;MathildeBadoual-
badoual@imnc.in2p3.fr;ChloeGerin-chloe.gerin@u-psud.fr;AlexandreGranier-alexandre.granier@mri.cnrs.fr;MarcLartaud-
lartaud@cirad.fr;
Correspondingauthor
Abstract
Background:
Digitalpathologyimagesareincreasinglyusedbothfordiagnosisandresearch,becauseslidescanners
arenowadaysbroadlyavailableandbecausethequantitativestudy oftheseimagesyieldsnewinsightsinsystems
biology. However,suchvirtualslidesbuildupatechnicalchallengesincetheimagesoccupyoftenseveralgigabytes
and cannot be fully opened in a computer’s memory. Moreover, there is no standard format. Therefore, most
commonopensourcetoolssuchasImageJ fail attreatingthem,andtheothersrequireexpensivehardwarewhile
still beingprohibitivelyslow.
Results:
Wehavedevelopedseveralcross-platform opensourcesoftwaretoolstoovercometheselimitations. The
NDPIToolsprovideawaytotransform microscopy imagesinitiallyinthelooselysupportedNDPIformatintoone
or several standardTIFF les, and to create mosaics (division of huge images intosmall ones, with or without
overlap) invarious TIFF andJPEG formats. They can be driven through ImageJ plugins. The LargeTIFFTools
achieve similar functionalityfor hugeTIFFimages whichdonott into RAM.Wetesttheperformanceof these
toolsonseveraldigitalslidesandcomparethem,whenapplicable,tostandardsoftware. Astatisticalstudyofthe
cellsina tissuesamplefromanoligodendrogliomawasperformedonanaveragelaptopcomputertodemonstrate
theeciency ofthetools.
Conclusions:
Our open source software enables dealing with huge images with standard software on average
computers. They are cross-platform, independent of proprietary libraries and very modular, allowing them to
be used in other open sourceprojects. They haveexcellent performance in terms of execution speed and RAM
requirements. They openpromising perspectives both tothe clinician who wants to study a singleslide and to
theresearchteam or data centre who doimageanalysisofmany slidesona computer cluster.
Keywords:
Digital Pathology,Image Processing,Virtual Slides,Systems Biology,ImageJ,NDPI.
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Background
Virtual microscopy has become routinely used over
the last few years for the transmission of pathol-
ogy images (the so-called virtual slides), for both
telepathologyand teaching[1,2]. In more andmore
hospitals,virtualslidesareevenattachedtothepa-
tient’s le [3,4]. They have also a great potential
for research,especially in the context of multidisci-
plinary projects involving e.g. mathematicians and
clinicians who do not work at the same location.
Quantitative histology is apromisingnew eld, in-
volving computer-based morphometry or statistical
analysisoftissues[5{9]. Agrowingnumberofworks
report the pertinence of such images for diagnosis
and classication of diseases, e.g. tumours [10{14].
Databases of clinical cases [15] will include more
and more digitized tissueimages. This growing use
of virtual microscopy is accompanied by the devel-
opment of integrated image analysis systems oer-
ing both virtual slide scanning and automatic im-
age analysis,which makes integration intothedaily
practice of pathologists easier. See Ref. [16] for a
review of someofthesesystems.
Modern slide scanners produce high magnica-
tion microscopy images of excellent quality [1], for
instance at the so-called \40x"magnication. They
allow much better visualization and analysis than
lower magnication images. As an example, Fig-
ure1showstwoportions of aslideatdierentmag-
nications, 10x and 40x. The benet of the high
magnicationforbothdiagnosis andautomatedim-
age analysis is clear. For instance, the state of the
chromatin inside the nucleus and the cell morphol-
ogy, better seen at high magnication,are essential
to help the clinician distinguish tumorous and non-
tumorouscells. Anaccurate,non-pixelateddetermi-
nation of theperimeters ofthe cellnuclei is needed
formorphometryand statistics.
However, this technique involves the manipula-
tion of huge images (of the order of 10 billions of
pixels for a full-size slide at magnication 40xwith
asinglefocuslevel)forwhichtheapproachtakenby
most standard software,loadingand decompressing
thefullimageintoRAM,isimpossible(asingleslice
of a full-size slide needs of the order of 30 GiB of
RAM). As a result, standard open-source software
such asImageJ [17],ImageMagick [18]or Graphics-
Magick[19] completely fails or is prohibitivelyslow
whenusedontheseimages. Ofcourse,commercially
available softwareexists [16],but it is usually quite
expensive,andveryoften restrictedtoasingleoper-
atingsystem. Itusesproprietarysourcecode,which
is a problem if one wants to control or check the
algorithms and their parameters when doingimage
analysis forresearch.
In addition, many automated microscopes or
slide scanners store the images which they produce
intoproprietary or poorly documented le formats,
and the software provided by vendors is often spe-
cic to some operating system. This leads to sev-
eral concerns. First, it makes research based on
digital pathology technically more dicult. Even
when a project is led on a single site, one has of-
ten to use clusters of computers to achieve large-
scale studies of many full-size slides from several
patients [20]. Since clusters of computers are typ-
ically run by open source software such as Linux,
pathologyimagesstoredinnon-standardleformats
are a problem. Furthermore, research projects are
nowcommonlyperformedinparallelinseveralsites,
not to say in several countries, thanks to technol-
ogy such as Grid [21], and there is ongoing eorts
towards theinteroperability of information systems
used in pathology [3,22]. Second, proprietary for-
mats mayhinder the development of shared clinical
databases [15] and access of the general public to
knowledge, whereas the citizen should receive ben-
et of public investments. Finally, they may also
raisenancialconcernsand con ictsofinterest[23].
Therehavebeen recentattempts todeneopen,
documented, vendor-independent software [24,25],
which partly address this problem. However, very
largeimagesstoredintheNDPIleformatproduced
by some slide scanners manufactured by Hama-
matsu, such as the NanoZoomer, are not yet fully
supportedbysuchsoftware. Forinstance,LOCIBio-
Formats[25]ispresentlyunabletoopenimages,one
dimension of which is largerthan65k,anddoesnot
deal properly with NDPI les of more than 4 GiB.
OpenSlide[24]doesnotcurrentlysupporttheNDPI
format. NDPI-Splitter[26]needstoberun onWin-
dows and dependson aproprietarylibrary.
To address these problems, we have developed
open source tools which achieve two main goals:
readingand convertingimages in theNDPI lefor-
mat intostandard open formats such as TIFF,and
splitting a huge image, without decompressing it
entirely into RAM, into a mosaic of much smaller
pieces (tiles),each of which can beeasilyopenedor
processed by standard software. All this is realized
withhigh treatmentspeed onallplatforms.
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Implementation
Overview
ThemainsoftwareisimplementedintheCprogram-
minglanguageasseparate,command-linedrivenex-
ecutables. It is independent of any proprietary li-
brary. This ensures portability on a large num-
ber of platforms (we have tested several versions of
Mac OS X, Linux and Windows), modularity and
ease of integration into scripts or other software
projects.
It is complemented by a set of plugins for the
publicdomainsoftwareImageJ[17],implementedin
Java, which call the main executables in an auto-
maticway toenablean interactiveuse.
The LargeTIFFTools and NDPITools are based
on the open source TIFF [27] and JPEG [28] or
libjpeg-turbo [29] libraries. The NDPITools plug-
ins for ImageJ are based on the Java API of Im-
ageJ[17,30]and ontheopensourcesoftwareImage-
IO [31], and use the Java Advanced Imaging 1.1.3
library[32].
Basic functions
The basic functions are the following. They can
be performed even on a computer with a modest
amount of RAM (seebelow the \performance" dis-
cussion).
1. splittinga tiled TIFF le into multiple TIFF
les,oneforeachofthetiles(tiffsplittiles
program);
2. extracting(\cropping")quicklyagivenrectan-
gleofasupposedlytiledTIFFleintoaTIFF
orJPEG le (tifffastcropprogram);
3. splitting one or several TIFF le(s), pos-
sibly very large, into mosaic(s), with-
out fully decompressing them in memory
(tiffmakemosaicprogram);
4. converting a NDPI le into a standard
multiple-image TIFF le, tiled if necessary,
using upon request the BigTIFF format in-
troduced in version 4.0.0 of the TIFF li-
brary [27,33,34], and encoding magnication
and focus levels as TIFF\image description"
elds(ndpi2tiff program);
5. creating a standard TIFF le for all or part
of the magnication levels and focus levels
present in the given NDPI le (the user can
ask for specic magnication and focus levels
and for aspecicrectangular regionoftheim-
age),and,upon request,creatingamosaicfor
each imagewhich doesn’t t intoRAM or for
all images (ndpisplit program). The names
of the created les are built on the name of
the source le and incorporate the magnica-
tionandfocuslevels(and,inthecaseofmosaic
pieces,thecoordinates insidethemosaic).
Mosaics
AmosaicisasetofTIFFor JPEGles(thepieces)
whichwouldreproducetheoriginalimageifreassem-
bled together, but of manageable size by standard
software. Theuser can either specify the maximum
amount of RAM which a mosaic piece should need
tobeuncompressed (default: 1024MiB),ordirectly
specify the size of each piece. In the rst case, the
size of each piece is determined by the software. A
given amount ofoverlap between mosaic pieces can
be requested, either in pixels or as a percentage of
theimagesize. This is usefule.g. for cell counting,
not tomiss cells which lie onthelimitbetween two
adjacentpieces.
Usage
Standalone
Our tools can be used through the command
line (POSIX-like shell or Windows command inter-
preter), and thereforecan be very easily integrated
into scripts or other programs. Depending on the
tool,thepathsand lenamesofoneorseveralles,
in NDPIorTIFF format,havetobeprovided. Op-
tionscanbeaddedwiththeirargumentsonthecom-
mand line to modify the behavior of the programs
fromitsdefault. Theyareexplainedinthemessages
printed by theprograms run without arguments,in
Unix-style man pages,and on the web pages of the
project (see below in the Availability and require-
ments Section).
Under the Windows OS, one can click-and-drag
the NDPI le icon onto the icon of ndpi2tiff or
ndpisplit. Weprovideprecompiled binarieswhere
frequently-used options are turned on by default:
e.g.ndpisplit-mJ.exeproduces amosaicinJPEG
formataswith option -mJ.Theconversion result or
mosaic can be found in the same directory as the
originalNDPIimage.
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ImageJ integration
Inadditiontocommandlineuse,thendpisplitpro-
gram can bedriven through the NDPITools plugins
in ImageJ with a point-and-click interface, so that
previewingthecontentofaNDPI leat low resolu-
tion,selectingaportion,extractingitathighresolu-
tionandnallyopeningitinImageJtoapplyfurther
treatmentscanbedoneinaneasyandgraphicalway.
Figure2shows ascreen shot of ImageJ1.47mafter
extraction of a rectangular zone from a NDPI le.
Figure3explainswhathappenswhen theNDPIle
contains several levels of focalization: the preview
imageis displayed as astack.
When producing a mosaic, the user can request
that pieces be JPEG les. Since the File > Open
commandofversions1.xofImageJisunabletoopen
TIFF les with JPEG compression (one has to use
plugins), this is way to produce mosaics which can
beopenedbyclick-and-dragontothewindoworicon
ofImageJ whilestillsavingdisk spacethanks toef-
cientcompression. Figure4shows how themosaic
productionoptionscanbesetinsideImageJthrough
theNDPIToolsplugins.
Results and Discussion
Performance
Wecomparetheperformanceofourtoolsonseveral
fundamental tasks to standard, broadly available
softwarein representative examples and on broadly
available computers.
Making amosaic from a hugeimage.
We chose an 8-bit RGB colour JPEG-compressed
TIFFle of10316863232pixels originatingin the
digitization of a pathology slide. The original le
weighted 975.01 MiB. Loading this image entirely
intoRAMwould needatleast 310316863232=
18:2GiB and is presently intractableon most ifnot
alldesktopandlaptopcomputersofreasonablecost.
Thetaskwastoproduce,fromthisimage,amo-
saic of 64 pieces so that each one needs less than
512MiB RAMtoopen.
On a3.2GHz IntelCorei3IMac computerwith
16 GB of RAM, the convert command from Im-
ageMagick(version6.8.0-7withquantumsize8bits)
was unable to completethe request. GraphicsMag-
ick (gm convert -crop; version 1.3.17 with quan-
tumsize8bits)completedtherequestin70min,us-
ing25GiBofdiskspace. tiffmakemosaicfromour
LargeTIFFTools completedtherequestin 2.5min.
To ascertain that this task can be equally
achieved even on computers with a modest RAM
amount,weperformedthesametaskona6-year-old
2.66 GHz Core2Duo Intel IMac with 2 GiB RAM.
Thetask wascompleted in9.0min.
Converting NDPIintoTIFF.
Splitting a NDPI le into TIFF les. A pathology
sample(6.7cm
2
oftissue)wasscannedatmagnica-
tion 40x and with 11 focus levels (every 2microns)
byaNanoZoomer,resultingina6.5GiBlein pro-
prietaryNDPIformat(calledle a.ndpihereafter).
Ona2.6GHzIntelCorei7MacMinicomputerwith
16 GiB RAM, ndpisplit extracted all 55 images
(11 focus levels and 5 magnications) as indepen-
dent, single-image TIFF les with JPEG compres-
sionin 7.11min. Thesizeofthelargest imageswas
18022470144. The speed was limited onlyby the
rateofI/OtransferssincetheCPUusageofthistask
was1.38min,outofwhichthesystemused1.30min.
Executingagainthesametaskstraightaftertherst
executiontook only 0.57min becausetheNDPIle
was still in thecache of the operatingsystem.
To ascertain that this task can be equally
achieved even on computers with a modest RAM
amount, we made a try on a 6-year-old 2.66 GHz
Core2Duo Intel PC with 2 GiB RAM running 32-
bits Windows XP Pro SP3. The original le shown
in Figure1, called b.ndpi,and weighting 2.07GiB
(largestimage: 10316863232pixels),wassplitinto
independentTIFFlesin2.2minwithoutswapping.
Incomparison,theLOCIBio-Formatspluginsfor
ImageJ[25],in its version 4.4.6with ImageJ1.43m,
was notabletoopentheimages in le a.ndpi even
atlow resolution.
ConvertingaNDPIleintoamultiple-imagesTIFF
le. Alternatively, the same proprietary-format le
a.ndpi was converted into a multiple-images TIFF
le with ndpi2tiff. On thesame computer as be-
fore, the conversion time was 7.0 min. Here again,
thespeedoftheprocessislimitedonlybytherateof
I/O transfers since the conversion took only 30s if
performedwhen the NDPIlewasstillinthecache
oftheoperating system.
Since the resulting TIFF le could not store all
55imagesinlessthan4GiB,wepassedtheoption-8
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onthecommand linetondpi2tifftorequestusing
the BigTIFF format extension. The specications
of this extension to the TIFF standard, discussed
and published before2008[33,34],aresupportedby
LibTIFFasofversion4.0.0[27],andthereforebythe
abundantimage viewingand manipulation software
which relieson LibTIFF. If theuseof the BigTIFF
formatextensionwouldhaveimpededthefurtherex-
ploitation of the produced TIFFle, wecould have
simplyused ndpisplitasabove. Orwe could have
called the ndpi2tiff command several times, each
time requesting extraction of asubset of all images
byspecifyingimagenumbersafterthelename,sep-
aratedwith commas,as in a.ndpi,0,1,2,3,4.
Extracting asmallregionfrom a hugeimage.
Thistaskcanbeusefultovisualizeatfullresolution
aregion of interest which the user has selected on
a low-magnication preview image. Therefore, it
should beperformed as quickly as possible.
From aTIFFle.
The task was to extract a rectangular region of
size 256256 pixels situated at the bottom right
corner of huge TIFF images and to save it as an
independent le. The source images were single-
image TIFFles using JPEG compression. Table 1
compares the time needed to complete the task
with tifffastcrop from our LargeTIFFTools and
with several software tools, on increasingly large
TIFFles. Testswereperformedona2.6GHzIntel
Corei7MacMinicomputerwith16GBofRAMand
used GraphicsMagick 1.3.17, ImageMagick 6.8.0-
7 and the utility tiffcrop from LibTIFF 4.0.3.
Noticeably,when treatingthelargestimage,Graph-
icsMagick needs 50GiB of freedisk space,whereas
tifffastcropdoesn’tneed it.
From aNDPI le.
Thetask wastoextract arectangularregion of size
256256 from one of the largest images of thele
a.ndpi (size 18022470144). On a 2.6GHz Intel
Core i7 Mac Mini computer with 16 GB of RAM,
theexecutiontimewas0.12sforoneextract,andin
average 0.06 s per extract in aseries of 20 extracts
withlocationsdrawnuniformlyatrandominsidethe
whole image.
Applications
Integrationindigitalpathologyimageserversorvirtual
slide systems
TheNDPIToolsarebeingused in severalothersoft-
ware projects:
 in asystemforautomaticblur detection [2,4].
 in WIDE[22],todealwith NDPIles. WIDE
isanopen-sourcebiologicalanddigitalpathol-
ogyimage archivingand visualization system,
which allows the remote user to see images
storedinaremotelibraryinabrowser. Inpar-
ticular,thankstothefeatureofhigh-speedex-
tractionofarectangularregionbyndpisplit,
WIDE saves costly diskspace since it doesn’t
needtostoreTIFFlesconvertedfromNDPI
les inaddition tothelatter.
Exploitinga largeset of digitalslides
Intheframeworkofastudyaboutinvasivelow-grade
oligodendrogliomas reported elsewhere [8], we had
to deal with 303 NDPI les, occupying 122 GiB.
On a 3.2 GHz Intel Core i3 IMac computer with
16 GB RAM, we used ndpisplit in a batch work
to convert them into standard TIFF les, which
took only a few hours. The experimental -s op-
tion of ndpisplit was used to remove the blank
lling between scanned regions, resulting in an im-
portantdisk spacesavingand in smaller TIFFles
(one for each scanned region)which where easier to
manipulateafterwards. Then,foreach sample,Pre-
view.app and ImageJ were used to inspect the re-
sulting images and manually select the regions of
clinical interest. The corresponding extracts of the
high magnication images werethesubjectofauto-
mated cellcountingand otherquantitative analyses
using ImageJ. In particular, we collected quantita-
tivedataaboutedemaor tissuehyperhydration [8].
Thisquantityneededaspecicimageanalysisproce-
durewhich isnot oered bystandard morphometry
softwareand,unlikecelldensityestimates,couldnot
be retrieved by sampling afew elds of view in the
microscope. Therefore, virtual microscopy and our
toolswereessentialin this study.
Study of a whole slide of brain tissue invaded by an
oligodendroglioma
To demonstrate the possibility to do research on
hugeimagesevenwithamodestcomputer,wechose
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a 3-year-old MacBook Pro laptop computer with
2.66GHz IntelCore 2Duo and4 GiB of RAM.We
used ImageJ and the NDPITools to perform statis-
tics on the upper piece of tissue on the slideshown
inFigure1.
Sincethedigitalslideb.ndpiweighted2.07GiB,
with ahighresolution imageof 10316863232pix-
els, it was not possible to do the study in
a straightforward way.
We opened the le
b.ndpi as a preview image with the command
Plugins > NDPITools > Preview NDPI...
and
selected on it the left tissue sample.
Then
we used the command Plugins > NDPITools >
Custom extract to TIFF / Mosaic... and asked
forextractionasamosaicof16JPEGles,eachone
needingless than 1GiB ofRAM toopen,and with
an overlap of 60pixels. This was completed within
afew minutes. Then we applied an ImageJ macro
toeach ofthe16pieces toidentifythedark cellnu-
clei (those with high chromatin content), based on
thresholding the luminosity values of the pixels, as
shown in Figure 1. It produced text les with the
coordinates andsizeof eachcellnucleus.
Outofthe154240identiednuclei,1951werepo-
sitioned on the overlapping regions between pieces.
Using the overlap feature of our tools enabled to
properly detect these nuclei, since they would have
been cut by the boundary of the pieces of the mo-
saicinabsenceofoverlap. Weavoideddoublecount-
ingby identifyingthe pairs ofnucleisituated in the
overlapping regions and which were separated by a
distance smaller thantheirradius.
As shown in earlierstudies [7,10,11],thesedata
canbeused for research anddiagnosispurposes. As
an example, Figure 5 shows the distribution of the
distance ofeachcellnucleustoits nearestneighbor.
Thankstotheveryhighnumberofanalyzedcellnu-
clei, this distribution is obtained with an excellent
precision.
Conclusions
The LargeTIFFTools, NDPITools and NDPITools
plugins for ImageJ achieve eciently some funda-
mental functions on large images and in particular
digital slides, for which standard open source soft-
ware fails or performs badly. Theyenable both the
clinician toexamine asingle slide and the bioinfor-
matics research teamtoperformlarge-scaleanalysis
ofmanyslides,possiblyon computer grids[20].
To date, the LargeTIFFTools have been down-
loaded from more than 388 dierent IP addresses,
theNDPITools frommorethan1361addresses,and
the ImageJ plugins from more than 235 addresses.
Table2liststhedistribution ofthetargetplatforms
amongthe downloadsofthebinary les. It shows a
broadusageofthedierentplatformsbythecommu-
nity,emphasizingthe importance of cross-platform,
open sourcetools.
We have explained how the software was used
to study some microscopic properties of brain tis-
suewhen invaded by an oligodendroglioma,and we
havegiven an illustrativeapplication totheanalysis
ofa whole-size pathologyslide. This suggests other
promisingapplications.
Availability and requirements
a. LargeTIFFTools
 Project name: LargeTIFFTools
 Project
home
page:
http://www.
imnc.in2p3.fr/pagesperso/deroulers/software/
largetitools/
 Operating system(s): Platform indepen-
dent
 Programming language: C
 Other requirements: libjpeg,libti
 License: GNUGPLv3
b. NDPITools
 Project name: NDPITools
 Project
home
page:
http://www.
imnc.in2p3.fr/pagesperso/deroulers/software/
ndpitools/
 Operating system(s): Platform indepen-
dent
 Programming language: C
 Other requirements: |
 License: GNUGPLv3
For the convenience of users, precompiled bi-
naries are provided for Windows (32 and 64 bits),
MacOSX and Linux.
c. NDPITools plugins for ImageJ
6
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 Project name: NDPITools plugins for Im-
ageJ
 Project
home
page:
http://www.
imnc.in2p3.fr/pagesperso/deroulers/software/
ndpitools/
 Operating system(s): Platform indepen-
dent
 Programming language: Java
 Other requirements:
ImageJ 1.31s or
higher,Ant,JAI 1.1.3
 License: GNU GPLv3
Competing interests
Theauthorsdeclare thatthey havenocompetinginter-
ests.
Authors contributions
CD wrote thepaper. MLconceivedandimplementeda
rst version of the integration into ImageJ as a toolset
ofmacros. CD implementedthe softwareandwrotethe
documentation. CG, AG and ML contributed sugges-
tions to the software. CD, DA, AGandML performed
software tests. CD,MB, CG, AGandMLselectedand
providedhistological samples. CD performedthestatis-
tical analysis ofthe sample slide. All authors reviewed
themanuscript. Allauthorsreadandapprovedthenal
manuscript.
Acknowledgements
We thank F. Bouhidel and P. Bertheau for their help
with the slide scanner of the Pathology Laboratory of
the Saint-Louis Hospital in Paris, andC. Klein(Imag-
ingfacility,CordeliersResearchCenter{INSERMU872,
Paris)fortestsandsuggestions.
The computer, CPU, operating system, and pro-
gramming language names quoted in this article are
trademarksoftheirrespective owners.
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8
Figures
(a)
(b)
(c)
Figure 1 - Asample slide.
(a): macroscopic view of the whole slide(the blackrectangleon theleft is 1x2cm). (b,c): In uenceof the
magnication on the quality of results. (b): a portion of theslide scanned at magnication level10x. The
whitecontours show theresultofanautomaticdetectionofthedarkcellnucleiwiththeImageJsoftware. A
signicantfractionofthecellnucleiismissedandthecontoursareratherpixelated. (c): thesameportionof
theslidescanned atmagnication40x. Thewhitecontoursshowtheresultofthesameautomaticdetection.
Almostall cellnucleiaredetectedand theshapes of the contours aremuch moreprecise. Scale bar: 4 m.
Figure 2 - Atypical session using ImageJ and the NDPITools plugins.
ANDPIlehas been openedwith the NDPITools plugins and it is displayed asapreview image (imageat
largest resolution which stilltsintothecomputer’s screen)| topwindow. Arectangular regionhas been
selectedand extracted asaTIFFimage,then opened | bottom window.
9
Figure 3 - Previewimageof a NDPI lewith several focalization levels in ImageJ.
The NDPI le 08.ndpi contains images at 5 dierent focalization levels. Therefore, its preview image is
displayed asastackof5images.
10
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