digital pathology solutions conference [email protected] toward a diagnosis assistance system for...

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


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?


ExampleExample

237 Mb


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


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


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


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.


• Multiparametric Analysis CAD system 1st version

• Spectral Analysis CAD system 2nd version



Strategy ExplorationStrategy Exploration


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


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


► 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


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


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


Strategy ExplorationStrategy Exploration




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)


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


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


IDC FA

ILC

CC

250 x 3 x 4 = 3000 retained patches

250 patches from each VS250 patches from each VS


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


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

• 1) elimination of the background


• 2) Cutting in 32x32 patches


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

φ1 = 0


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

Stroma Cellular zones

φ1 = 0


Visual control

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


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


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


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.


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


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".

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