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9 2 Goals of the Ph.D

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Contents

I Introduction: Medical context and goal of the thesis 1

1 Context of the Thesis 3

1.1 Importance of tissue-based biomarkers in histopathology . . . . 3 1.2 Whole slide scanners . . . . 6 1.3 From digital pathology to computational pathology . . . . 7 1.4 Tissue microarrays and multiplex immunohistochemistry: contribution to com-

putational pathology . . . . 9

2 Goals of the Ph.D. Thesis 15

2.1 Goal 1: Automating TMA core identification . . . 16 2.2 Goal 2: Providing digital solutions to the inter-batch variability of immunohisto-

chemical staining . . . 16 2.3 Goal 3: Compartmentalizing IHC quantification . . . 17

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2.4 Goal 4: Analyzing multiplex IHC assays and staining colocalization . . . 18

II Original developments 21 3 Overview of the original developments 23 4 Core detection and identification for automating TMA analysis 29 4.1 ROIs in virtual TMA slides . . . 30

4.2 Method of automatic grid fitting on tissue micro-array images . . . 31

4.3 Method validation and uses . . . 35

5 Inter-batch staining normalization 37 5.1 Causes of IHC staining variability . . . 38

5.2 State of the art . . . 41

5.3 Methods . . . 44

5.4 Experimental design for quantitative evaluation . . . 52

5.5 Results . . . 56

5.6 Discussion . . . 70

6 Gland segmentation to compartmentalize IHC quantification 75 6.1 Previous work and novel contributions . . . 76

6.2 Methods . . . 78

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6.3 Evaluation methodology and results . . . 87

6.4 Discussion and conclusion . . . 102

7 Image registration and colocalization 107 7.1 Registration of serial TMA slides . . . 108

7.2 Colocalization measurements: from fluorescence to brightfield IHC . . . 112

7.3 Validation of colocalization measurements for IHC biomarkers . . . 114

7.4 discussion and conclusion . . . 121

III Future works and conclusions 123 8 Future work 125 8.1 Using IHC biomarkers to generate supervised training sets . . . 126

8.2 Compartimentalized multimarker analysis . . . 128

8.3 Improving the deep neural network architecture developed during this thesis . . . 128

9 Discussion and Conclusions 131 9.1 Normalization and data augmentation . . . 132

9.2 On the current limitations of deep learning . . . 133

9.3 Deep learning at the service of pathology. . . 136

References 139

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