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Page 1: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

TOWARD A DIAGNOSIS TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR ASSISTANCE SYSTEM FOR

DIGITAL PATHOLOGY OF BREAST DIGITAL PATHOLOGY OF BREAST CANCERCANCER

M. Oger, P. Belhomme, J.J. Michels, A. Elmoataz

GRECAN, EA 1772,University of Caen Basse-NormandieF. BACLESSE Cancer Centre, CaenGREYC, UMR 6072, University of Caen Basse-Normandie

Page 2: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

IntroductionIntroduction

• Identification of breast tumor lesions is not always a easy task.

• Cancer lesions are sometimes heterogeneous.

• Question: is automatic image processing able to help classifying benign and malignant breast lesions?

Page 3: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

ExampleExample

237 Mb

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Digital Pathology Solutions Conference [email protected]

AimAim

• To try to develop automatedComputer-Aided Diagnosis (CAD) toolsfor pathologists

• To work with Virtual Slides (VS) in order to take into account lesion heterogeneity

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Digital Pathology Solutions Conference [email protected]

Material and methodMaterial and method

• Low resolution Virtual Slide6 µm: Nikon CoolScan 8000 ED.

• 224 images (different size) are included in the knowledge base

• 28 histological types• 3 histological families (Benign, Malignant Carcinoma,

Malignant Sarcoma)

slide holder

images with foci of different histological type exist, but we labeled them according to the dominant type

Page 6: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

Example of low resolution VSExample of low resolution VS

• At the resolution of 6 µm, pathologists recognize fairly easily histological types in 80 to 90% of cases.

but “small objects” are sometimes difficult to identify

Fibroadenoma Intraductal carcinoma

2228 X 1915 px = 12.3 Mb3479 X 2781 px = 28 Mb

Page 7: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

Material and methodMaterial and method

• A “new image” will be compared to the knowledge database.

• A graphical user interface will be built to allow a “visual” presentation of the results obtained.

Page 8: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

• Multiparametric Analysis CAD system 1st version

• Spectral Analysis CAD system 2nd version

• Multiparametric Analysis CAD system 1st version

• Spectral Analysis CAD system 2nd version

Strategy ExplorationStrategy Exploration

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Digital Pathology Solutions Conference [email protected]

Multiparametric analysisMultiparametric analysis

• We have developed a system which statistically determines the “similarity degree” of a new image compared to the different histological types.

• Requirements: » No segmentation

» Exploration of several color spaces: RGB, YCH1CH2 (Carron), AC1C2 (Faugeras), I1I2I3 (Ohta)...

• Application:» Computing a “signature” of parameters of the whole VS

» Comparing the signatures

Page 10: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

The color signaturesThe color signatures• 234 global parameters computed on 6 color spaces

– Histograms– Mean– Median– Kurtosis– Skewness…

• + 13 "texture" parameters– S/N measure– Haralick…

• Vector distance (comparison of signatures) – Kullback-Leibler distance

• Software development– PYTHON language

n

i x

yy

y

xxdivKL

1

log.log.

Principal Component Analysis 188

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► Automated systemAutomated system► InputInput = a new image = a new image► OutputsOutputs = similar = similar

imagesimagesfrom the knowledge from the knowledge basebase

CAD 1st version CAD 1st version systemsystem

Page 12: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

Rank of the first image of the

same type

11 13.99 %13.99 %

≤ ≤ 33 33.33 %33.33 %

≤ ≤ 55 47.74 %47.74 %

≤ ≤ 1010 67.08 %67.08 %

Exhaustive analysis of the image database (one image vs the 223 others)with Kullback-Leibler distance

CAD 1st version: CAD 1st version: ResultsResults

Page 13: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

CommentsComments

• Low resolution image classification is possible butthis strategy is a crude one which can lead only to a “preclassification” of the lesion under study

• Other strategies are to be explored

Page 14: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

Strategy ExplorationStrategy Exploration

• Multiparametric Analysis CAD system 1st version

• Spectral Analysis CAD system 2nd version

Page 15: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

Principle of spectral techniques Principle of spectral techniques for structural analysis of an for structural analysis of an

image databaseimage database

• Working on images with identical size• Comparing “point to point” each image with all

those of the database ==> the signature is the WHOLE image

• Trying to determine a “distance” between all the images of the database by using techniques of Spectral Dimensionality Reduction

• Replacing a n-dimensional space by a2D-visualization space (φ1, φ2)

Page 16: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

Application to breast lesionsApplication to breast lesions• Problem:

– Database images are of various size

– In an image, some areas are uninformative (stroma, normal tissue, adipose cells...)

• Proposed solution: – Finding the interesting

“PATCHES” which describe the histological type at best

– Choosing an adequate size for “patches”: 32x32 px²

Page 17: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

Example of 4 distinct classesExample of 4 distinct classes

• We work with:– Intra Ductal Carcinoma– Invasive Lobular Carcinoma– Colloid Carcinoma– Fibroadenoma

• We take only the 3 most representative VS of each class(□) 12 VS among 73

Invasive Lobular Carcinoma

Intra Ductal Carcinoma

Fibroadenoma

Colloid Carcinoma

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Digital Pathology Solutions Conference [email protected]

IDC FA

ILC

CC

250 x 3 x 4 = 3000 retained patches

250 patches from each VS250 patches from each VS

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Graph of the Graph of the selectedselected 4 types 4 types

Invasive Lobular Carcinoma

Fibroadenoma

Colloid Carcinoma

Intra Ductal Carcinoma

1 cross per patch = 3000 crosses

Page 20: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

How can we analyseHow can we analysea a ““new imagenew image””

• 1) elimination of the background

Page 21: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

• 2) Cutting in 32x32 patches

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• 3) « patches » are projected on a 2D space (φ1, φ2)

φ1 = 0

Page 23: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

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• 4) segmentation by spectral analysis:patches corresponding to stroma are removed (cellular zones are preserved)

Stroma Cellular zones

φ1 = 0

Page 24: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

Visual control

• 4) segmentation by spectral analysis:patches corresponding to stroma (Green) are removed, cellular zones (Purple) are preserved

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CAD 2nd versionCAD 2nd version • 5) cellular patches

of the new image are projected onto the graph of cellular patches of the 4 histological types

Insertion of the new image

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Digital Pathology Solutions Conference [email protected]

CAD 2nd versionCAD 2nd version

Intra Ductal Carcinoma 42,37%

Invasive Lobular Carcinoma 5,64%

Colloid Carcinoma 29,98%

Fibroadenoma 22,01%

Matching probabilities

2-neighborhood k-neighborhood

Results of a test done with a “new image” corresponding to an

Intraductal Carcinoma

Detail of the whole graph

Page 27: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

ConclusionConclusion

• Technique of spectral analysis seems to be promising regarding 4 classes of tumors.

• This technique could be applied in order to try to identify tumor foci of different types on a virtual slide.

Page 28: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

PerspectivesPerspectives

• But a lot of work remains to be done:– Extending the spectral analysis to 28 classes (the rest

of the database): improving the separation of the influence zone of each histological type.

– Increasing the signature: image patch + parameters which have been selected in the first part.

– Testing a higher resolution (sub sampled high resolution virtual slides).

Remark: the final strategy will be easily applicable to other tumor locations

Page 29: Digital Pathology Solutions Conference m.oger@baclesse.fr TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme,

Digital Pathology Solutions Conference [email protected]

Acknowledgements:

The authors gratefully acknowledge

Dr Paulette Herlin, Dr Benoît Plancoulaine,Dr Jacques Chasle,

the Regional Council of "Basse-Normandie"

and the "Comité départemental du Calvados de la Ligue de Lutte Contre le Cancer".