Digital Pathology Solutions Conference 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

Digital Pathology Solutions Conference 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?

Digital Pathology Solutions Conference 237 Mb

Digital Pathology Solutions Conference Aim To try to develop automated Computer-Aided Diagnosis (CAD) tools for pathologists To work with Virtual Slides (VS) in order to take into account lesion heterogeneity

Digital Pathology Solutions Conference Material and method Low resolution Virtual Slide 6 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

Digital Pathology Solutions Conference 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 Fibroadenoma Intraductal carcinoma 2228 X 1915 px = 12.3 Mb3479 X 2781 px = 28 Mb

Digital Pathology Solutions Conference 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.

Digital Pathology Solutions Conference Multiparametric Analysis  CAD system 1 st version Spectral Analysis  CAD system 2 nd version Multiparametric Analysis  CAD system 1 st version Spectral Analysis  CAD system 2 nd version Strategy Exploration

Digital Pathology Solutions Conference 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

Digital Pathology Solutions Conference 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 Principal Component Analysis 188

Digital Pathology Solutions Conference ► Automated system ► Input = a new image ► Outputs = similar images from the knowledge base CAD 1st version system

Digital Pathology Solutions Conference Rank of the first image of the same type % ≤ % ≤ % ≤ % Exhaustive analysis of the image database (one image vs the 223 others) with Kullback-Leibler distance CAD 1st version: Results

Digital Pathology Solutions Conference Comments Low resolution image classification is possible but this strategy is a crude one which can lead only to a “preclassification” of the lesion under study Other strategies are to be explored

Digital Pathology Solutions Conference Strategy Exploration Multiparametric Analysis  CAD system 1 st version Spectral Analysis  CAD system 2 nd version

Digital Pathology Solutions Conference 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 a 2D-visualization space (φ1, φ2)

Digital Pathology Solutions Conference 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

Digital Pathology Solutions Conference 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 Invasive Lobular Carcinoma Intra Ductal Carcinoma Fibroadenoma Colloid Carcinoma

Digital Pathology Solutions Conference IDC FA ILC CC 250 x 3 x 4 = 3000 retained patches 250 patches from each VS

Digital Pathology Solutions Conference Graph of the selected 4 types Invasive Lobular Carcinoma Fibroadenoma Colloid Carcinoma Intra Ductal Carcinoma 1 cross per patch = 3000 crosses

Digital Pathology Solutions Conference How can we analyse a “new image” 1) elimination of the background

Digital Pathology Solutions Conference 2) Cutting in 32x32 patches

Digital Pathology Solutions Conference 3) « patches » are projected on a 2D space (φ1, φ2) φ1 = 0

Digital Pathology Solutions Conference 4) segmentation by spectral analysis: patches corresponding to stroma are removed (cellular zones are preserved) Stroma Cellular zones φ1 = 0

Digital Pathology Solutions Conference Visual control 4) segmentation by spectral analysis: patches corresponding to stroma (Green) are removed, cellular zones (Purple) are preserved

Digital Pathology Solutions Conference CAD 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

Digital Pathology Solutions Conference CAD 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

Digital Pathology Solutions Conference 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.

Digital Pathology Solutions Conference 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

Digital Pathology Solutions Conference 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".