TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER

1. TOWARD A DIAGNOSIS ASSISTANCE SYSTEM FOR DIGITAL PATHOLOGY OF BREAST CANCER M. Oger, P. Belhomme, J.J. Michels, A. Elmoataz GRECAN, EA 1772,University of Caen Basse-Normandie F. BACLESSE Cancer Centre, Caen GREYC, UMR 6072, University of Caen Basse-Normandie

2. Introduction • 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?

3. Example 237 Mb

4. Aim • To try to develop automatedComputer-Aided Diagnosis (CAD) toolsfor pathologists • To work with Virtual Slides (VS) in order to take into account lesion heterogeneity

5. slide holder Material 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) images with foci of different histological type exist, but we labeled them according to the dominant type

6. Intraductal carcinoma Fibroadenoma 3479 X 2781 px = 28 Mb 2228 X 1915 px = 12.3 Mb Example 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

7. Material 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.

8. Strategy Exploration • Multiparametric Analysis CAD system 1st version • Spectral Analysis CAD system 2nd version • Multiparametric Analysis CAD system 1st version • Spectral Analysis CAD system 2nd version

9. Multiparametric 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

10. Principal Component Analysis 188 The 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

11. Automated system • Input = a new image • Outputs = similar imagesfrom the knowledge base CAD 1st version system

12. CAD 1st version: Results Exhaustive analysis of the image database (one image vs the 223 others) with Kullback-Leibler distance

13. Comments • 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

14. Strategy Exploration • Multiparametric Analysis CAD system 1st version • Spectral Analysis CAD system 2nd version

15. Principle of spectral techniques for structural analysis of an image 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)

16. Application 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

17. Intra Ductal Carcinoma Invasive Lobular Carcinoma Colloid Carcinoma Fibroadenoma Example 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

18. 250 patches from each VS FA IDC CC ILC 250 x 3 x 4 = 3000 retained patches

19. Graph of the selected 4 types Colloid Carcinoma Fibroadenoma Intra Ductal Carcinoma Invasive Lobular Carcinoma 1 cross per patch = 3000 crosses

20. How can we analysea “new image” • 1) elimination of the background

21. 2) Cutting in 32x32 patches

22. 3) « patches » are projected on a 2D space (φ1, φ2) φ1 = 0

23. Cellular zones Stroma φ1 = 0 • 4) segmentation by spectral analysis:patches corresponding to stroma are removed (cellular zones are preserved)

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

25. CAD 2nd version Insertion of the new image • 5) cellular patches of the new image are projected onto the graph of cellular patches of the 4 histological types

26. k-neighborhood CAD 2nd version Results of a test done with a “new image” corresponding to an Intraductal Carcinoma Matching probabilities 2-neighborhood Detail of the whole graph

27. Conclusion • 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.

28. Perspectives • 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

29. 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".