HETEROGENEITY ASSESSMENT OF HISTOLOGICAL TISSUE SECTIONS IN WHOLE SLIDE IMAGES

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1 12th European Congress on Digital Pathology HETEROGENEITY ASSESSMENT OF HISTOLOGICAL TISSUE SECTIONS IN WHOLE SLIDE IMAGES BELHOMME Philippe 1, TORALBA Simon 1,3, PLANCOULAINE Benoît 1, OGER Myriam 1,2, GURCAN Metin N. 3, BOR-ANGELIER Catherine 1,2 1 Normandie Université; UNICAEN, CLCC F. Baclesse, PATHIMAGE BioTICLA EA 4656, Caen, France 2 Pathology department, CLCC F. Baclesse, Caen, France 3 CIALab, BioInformatics Department, OSU, Columbus, Ohio, USA

2 Heterogeneity in a breast cancer problematic Breast cancer is the most frequent women cancer, and their 2 nd cause of cancer mortality in the world. Breast cancer is now earlier detected, and then the target of surgical removing, radiotherapy, with chemotherapy or hormonotherapy. In spite of these treatments, 30% of the patients relapse. Not taking into account and misunderstanding intra tumorous heterogeneity is seen by pathologists as a major cause of treatments failure. We propose to improve the reproducibility of its assessment by the help of computer aided quantitative image analysis.

3 An image analysis compatible with whole slide images (WSI) The advent of digital scanners leads to generate WSI from histological sections acquired at a full resolution. Image analysis (IA) provides quantitative and repeatable measurements by means of methods such as segmentation, indexation or classification. The size of WSI data to be processed then becomes very large and complicates segmentation operations. The original concept proposed is to not rely on segmentation but on classification.

4 TMAs Differences of tissue visualized in TMAs - without any segmentation

5 Architecture of the developed Computer-Aided Diagnosis System - WSI is split in small parts called patches - Database is created from texture and color features - Dissimilarities are computed for every pair of patches (using Kulback Liebler distance) - Dimensionality reduction

6 Dimensionality reduction Color and texture feature measurements : - 13 Haralick in 4 direction - 17 geostatistic properties - 6 histogram statistics - 4 quantiles on spectrum 79 items per color channel and per patch For every pair of patches on which we measured N features, we have a total dissimilarity D N written as: D N = D 1. f1 +D 2. f2 + +D N. fn Dimensionality reduction allows to represent theses N-D dissimilarities in M-D (M<<N). Compared to other methods such as PCA, diffusion maps preserves local structures.

7 Architecture of the developed Computer-Aided Diagnosis System - WSI is split in small parts called patches - Database is created from texture and color features - Dissimilarities are computed between each patch (using Kulback Liebler distance) - Dimensionality reduction Patches projected in a reduced space Visualization Classification Spatial heterogeneity

8 Patches projected in a reduced space

9 A visualization in pseudo-color space Patches projected in a reduced space The 3-D coordinates of a patch can be assimilated to a pseudo-color if the 3 major axes are thought as color channels. Each patch is a big pixel in the WSI. All classical Image analysis operators can be applied on these pixels. reduced space Original image RGB HSL

10 Patches projected in a reduced space D 3 D3 = euclidean distance in the reduced 3D space between two patches = dissimilarity of two patches

11 4-connected Patches projected in a reduced space local method Visualization of mean dissimilarities of 4-connected patches. We compute the energy of this image, considered as a quantification of local heterogeneity. n E = mean(4c dissimilarities ) 2 k=0

12 Bresenham linear method Method inspired from Brooks and Grigsby (2013): quantification of heterogeneity on greyscale images along every line of the image. Average of differences between line profile on image and most homogenous profile possible.

13 In our case, we take the dissimilarity between the two ends of the line as an homogenous model. To this we compare the sum of dissimilarities between consecutive patches situated on the line. A B C D E F G h AK = d AB + d BC + + d IJ + d JK d AK H I J K Possibility to classify measures according to line length. Exemple : A C B C A D D B

14 Rao s Quadratic Entropy Originally created to quantify the diversity of species in an ecosystem, from prevalence p and taxonomic difference d between I and j species. N QE = p i p j d ij 2 i>j=1 In our case, we consider every patch as unique, getting their prevalence to 1/N. Taxonomic difference corresponds to patch dissimilarities. N QE = 1 N 2 d ij i>j=1

15 Results 1 slightly 3 greatly 2 4 slightly moderately. heterogeneous

16 Results , Image 1 0,7 0,6 0, Image 2 Image 3 Image 4 0,4 0,3 0,2 1 Image 1 Image 2 Image 3 Image Lenght (patches) 0,1 0 Image 1 Image 2 Image 3 Image 4 4-C local method Bresenham linear method Quadratic Entropy

17 Conclusions We proposed a framework decomposed in several steps : - No segmentation (images are split into patches), - Based on feature extraction (color and texture) - Dissimilarity measurement - Dimensionnality reduction (diffusion maps) - Heterogeneity assessement (local, regional, global scales) This general structure is opened to integration of other methods (svm, acp, other non linear dimensionnaly reductions, other features).

18 Thanks for your attention Merci de votre attention

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20 Calibration of DIADEM space Each DIADEM analysis creates a specific space of dissimilarity, making impossible comparison of several sets of patches. We state that 2 space have a linear geometric link on condition that data is not too different. Then, this link can be deducted from the coordinates of a minimum of 3 patches common to each analysis. These 3 spy patches are chosen as the 3 most representative patches of the first DIADEM space. The relation between two spaces is obtained by resolving 3 systems of 3 equations with 3 unknowns.

21 Calibration of DIADEM space - test Initial image, composed of 4 Brodatz textures, is cut in two parts. 2. Initial image analysis Integration of 3 «spy» patches from initial image in other patch sets. 4. Analysis of patches from the two parts added of 3 spies Calibration of the two parts onto initial image computed from spies coordinates. 6. Merging of two calibrated analysis. 7. Differences measurements : 21% max, 6% mean But visually convincing