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Practical Image Analysis from a Pathologist’s Perspective

Frank A. Voelker, DVM, DACVP Pathology Experts LLC. Practical Image Analysis from a Pathologist’s Perspective. Topics……. Introduction General Concepts and Approaches Guidelines and Pitfalls Analytical Strategies Applications and using Genie™ Summary. General Analytical Approaches…….

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Practical Image Analysis from a Pathologist’s Perspective

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  1. Frank A. Voelker, DVM, DACVP Pathology Experts LLC Practical Image Analysis from a Pathologist’s Perspective

  2. Topics……. • Introduction • General Concepts and Approaches • Guidelines and Pitfalls • Analytical Strategies • Applications and using Genie™ • Summary Image Analysis in Pathology

  3. General Analytical Approaches……. Pixel Count IHC Deconvolution Co-localization IHC Nuclear Rare Event Membrane Angiogenesis Image Analysis in Pathology

  4. Two Different Approaches for Analysis Quantify Histomorphologic Change • Cellular Hypertrophy/Atrophy • Cell Numbers • Tissue Infiltrates (eg. Fibrosis) • Other Structural Alterations Usually measuring area or number Quantify Substances using Special Stains Usually measuring area and/or intensity • Histochemistry • IHC • ISH Image Analysis in Pathology

  5. pS6 Ser235 Immunostain of Breast Carcinoma Introducing the Concept of “Targeted Cell” Analysis Analysis of average cytoplasmic stain intensity using the pixel count tool may be useful in evaluating a neoplasm if there is little background or nonspecific staining. Image Analysis in Pathology

  6. Fibrosis in Livers of Zucker Rats Use of the Positive Pixel Count Tool enables “visually apparent” analysis of a change T T Fenofibrate Rat No. 5 Control Rat No. 12 T T C Pioglitazone Rat No. 3 Compound X Rat No 2 C X F P Variations in fibrosis (blue) about small portal triad veins (T) as depicted using Masson’s Trichrome stain Image Analysis in Pathology

  7. Quantitation of PAS Stain for Glycogen in Livers of DIO Mice Administered XXX Using the Aperio Color Deconvolution Tool Using the Color Deconvolution Tool enables quantitation of things visually obscured by counterstaining PAS-stained Section Aperio Markup Image Image Analysis in Pathology

  8. Cyclin D1 Immunostain of Human Breast Carcinoma Use of the IHC Nuclear Analysis Tool to Determine Percent and Degree of Positivity of Neoplastic Cell Nuclei. Stromal Nuclei are Excluded from Evaluation.

  9. Quantifying Inflammation in Tissue using the Nuclear Analysis Tool… Different cell types often can be distinguished from each other in the same tissue based on nuclear diameter. Here lymphocyte nuclei are smaller than mammary carcinoma nuclei. This makes it possible to count lymphocyte numbers per unit area of tissue cross section to determine degree of infiltration. Algorithm: IHC Nuclear (cell-based) Image Analysis in Pathology

  10. Mouse Liver - Hepatocellular Hypertrophy Drug-related enzyme induction leading to increases in cytoplasmic endoplasmic reticulum with resultant hepatocyte size increase. Total Hepatocyte Nuclei = 199 Average Nuclear Size =140 µm² 706 nuclei/mm² Total Hepatocyte Nuclei = 167 Average Nuclear Size = 160 µm² 508 nuclei/mm² Algorithm: IHC Nuclear (cell-based) Image Analysis in Pathology

  11. Some Guidelines for Analysis of Slides from Experimental Studies • Take care to assure immediate optimal fixation for all tissue samples. Uniformity of handling as well as fixation time is important. • Staining procedures for all slides in a study need to be performed simultaneously in a single batch to assure uniformity of stain. • Sampling must be strictly representational as well as consistent. Care must be taken to assure exact uniformity of analysis with respect to anatomical location (eg. Tissue trimming, sectioning) • A preliminary evaluation of image analysis tools between some slides of varying stain intensities will help assure that analysis values are established optimally for all slides in the study. Image Analysis in Pathology

  12. Anatomic Consistency in Sampling….. Image Analysis in Pathology

  13. Sirius Red Stain Depicting Myocardial Fibrosis in a Mouse Analysis Tool: Color Deconvolution (area-based) Precision in level of section is required for accurately comparing amounts of fibrosis between treatment groups Image Analysis in Pathology

  14. Consistency of Sample Area Selection for Morphometric Analysis within the Median Lobe of the Mouse Liver 1 2 3 Select samples within approximately the same region of the same lobe of the liver for consistency of analysis. As an assurance of sampling homogeneity, areas should have roughly similar pixel count values. Image Analysis in Pathology

  15. Consistency of Study Conditions can Affect Morphometric Analysis Variations in duration of fasting prior to necropsy can result in large differences in hepatocellular glycogen thus leading to inaccurate analysis 212 nuclei/mm² 263 nuclei/mm² Mouse Livers Image Analysis in Pathology

  16. Three Possible Strategies for Measuring Brown Stains using the Positive Pixel Count Analysis Tool • Quantitate the percentage area of all brown pixels in the section or in selected areas of the section. • If the chromagen staining is very extensive in the target cell population, measure only the brownest (darker) pixels in selected areas of the section. • If the chromagen staining is uniform in character and very extensive in the target cell population, measure stain intensity as an index of concentration. Image Analysis in Pathology

  17. Percent of Liver Tissue Staining for Transferrin Receptor(CD71) in Female Mice by Immunohistochemistry Measuring all of the brown pixels in the sample area ** % * Control 100 mg/kg 250 mg/kg 1000 mg/kg * p  .01 **p  .001 Image Analysis in Pathology

  18. Quantitation of Cytochrome p450 Reductase in Centrilobular Hepatocytes Despite Widespread Immunostaining Original Image Markup Image Measuring only the area of more intense stain Color deconvolution (area-based)

  19. Quantitation of VEGF Immunostaining in Livers of Mice administered XXX for 52 Weeks Control Females Control Males 1000 mg/kg Males 1000 mg/kg Females Comparing stain intensity Image Analysis in Pathology

  20. The Challenge of Analyzing only the Target Tissue……. PTEN Immunostain of Squamous Cell Carcinoma in Human Lung Variable staining of neoplasm and staining of surrounding stroma make morphometric analysis difficult. Image Analysis in Pathology

  21. Automated Recognition of Neoplastic Components in a Human Bronchoalveolar Carcinoma using Genie™ Recognition of neoplastic tissue components within a neoplasm is an important first step in quantifying amounts and intensities of specific biomarkers using IHC. This is needed for accurate clinical trial assessment of antineoplastic agents The next step would be to link neoplastic tissue recognition using Genie™ with a color deconvolution tool for measurement of chromagen in an IHC stain. Image Analysis in Pathology

  22. Genie™…….. Introducing the concept of using histology pattern recognition software as a preprocessing machine to segregate target from nontarget tissue during analysis Strategies Image Analysis in Pathology

  23. Steps in Chromagen Analysis of a Neoplasm (Excluding the Stroma) Primary IHC image Genie™markup with selection of neoplasm 1 2 Eliminate stroma Final Aperio ImageScope deconvolution markup 4 3 Image Analysis in Pathology

  24. Quantitation of Splenic Extramedullary Hematopoiesis in a Mouse using Genie™ and the Aperio Positive Pixel Count Tool Genie™Markup Image H&E Stain Positive Pixel Markup Image Results: EMH comprises 50.2% positive pixels in evaluation area Image Analysis in Pathology

  25. Quantitation of Periarteriolar Lymphoid Tissue in a Mouse Spleen using Genie and the Aperio Positive Pixel Count Tool H&E Stain Genie Markup Image Aperio Positive Pixel Markup Image Result: Lymphoid tissue comprises 30.1% of positive pixels in splenic cross-sectional area Extrapolating to an entire tissue section demands more robust training than for a simple image. Image Analysis in Pathology

  26. Analysis of Study Sample Groups by Genie™ Morphologically Variable Samples Trained Individually for Genie Target Tissue Selection Targeted Tissue Selection and Isolation by Genie™ Subsequent Uniform Analysis of Isolated Target Tissue for area/intensity Separate target tissue training of each sample does not adversely affect final analysis. Image Analysis in Pathology

  27. Bile Duct Hyperplasia in Rat Liver First pass Genie histology pattern identification with minimal training. Genie™ can simultaneously analyze three or more tissue areas Hyperplastic Bile Ducts = Green Hepatic Parenchyma = Red Periportal Inflammatory Cells = Blue Periductal Collagen = Brown Bile Duct Lumena + Sinusoids = Yellow Then analyze up to three tissue areas using colocalization tool Image Analysis in Pathology

  28. Quantitation of Hepatocellular Necrosis Use of Genie™ as a preprocessing utility to identify regions of hepatic necrosis (red) and areas of normal liver (green) Subsequent quantitation of necrotic areas using a pixel count tool to allow precise grading Image Analysis in Pathology

  29. Using Genie™ to Discriminate Between Nuclear and Cytoplasmic Markers Human Breast Carcinoma Stained for Estrogen Receptor The ability of Geni to discriminate between nuclear and cytoplasmic regions of a neoplasm allows separate biomarker intensity measurement for both nuclear and cytoplasmic markers. Image Analysis in Pathology

  30. Monkey Lung Use of Genie™ as a preprocessing utility to identify regions of smooth muscle (green) Subsequent quantitation of pulmonary smooth muscle using a pixel count tool Image Analysis in Pathology

  31. Cynomolgus Monkey Lung Use of Genie™ as a preprocessing utility to identify regions of bronchiolar epithelium (green) Subsequent isolation and analysis of only bronchiolar epithelium using the positive pixel count or other analysis tool Image Analysis in Pathology

  32. Islet Cell Mass of Mouse Pancreas Measurement of Pancreatic Islet Cell Mass using Genie™ Followed by the Colocalization Algorithm (A/B)C=Islet Cell Mass A=Total Islet Area in Section B=Total Pancreas Area in Section C=Pancreatic Weight Image Analysis in Pathology

  33. Estimating Islet Cell Hypertrophy in the Mouse Pancreas Calculating Mean Islet Cell Area using Genie™ followed by the IHC Nuclear Algorithm Total islet area = 103014 µm² Total number islet nuclei = 575 103014 µm²/575 =179 µm²/islet cell Image Analysis in Pathology

  34. Quantitating Dog Thyroid Gland Tissue Components Use of Genie™ as a preprocessing utility to identify thyroid gland follicular epithelium (green), colloid (red) and C-cells (blue) Then quantitate each separate tissue component area using the colocalization tool. Image Analysis in Pathology

  35. Measuring Cellular Hypertrophy of two cell types in a Dog Thyroid Gland Set Genie™ masks for brown follicular epithelium and blue c-cells. Then apply colocalization tool to calculate respective areas of each. Then apply IHC nuclear tool on same image to get numbers of artificially colored brown and blue nuclei. Total Brown Area/Total Brown Nuclei = Mean Follicular Cell Area. Do same calculation for blue nuclei. Image Analysis in Pathology

  36. Summary • The ability to digitize entire slides and perform morphometric analysis on images has been valuable in allowing the rapid and practical measurement of tissue biomarkers for pharmaceutical research and development. • A number of strategies and examples have been presented for using various image analysis algorithms in the measurement of tissue changes and tissue biomarkers. Image analysis of specific target tissues can be particularly challenging in cases with large and morphologically intricate areas of tissue, or when tissue staining is nonspecific. • Genie™, a histology pattern recognition tool, has been introduced as a preprocessing utility capable of identifying and categorizing specific histologic tissue types, thus allowing subsequent analysis of target regions by standard image analysis tools. • Significant challenges remain in developing practical procedures and methods appropriate for the analysis of oncology and toxicology specimens. Recent object recognition advancements may assist in this effort. Image Analysis in Pathology

  37. Acknowledgements • Ms. Kimberly Merriam, TBG, BMD Novartis • Ms. Jeanette Rheinhardt, TBG, BMD Novartis • Dr. Allen Olson, Aperio • Dr. Kate Lillard-Wetherell • Mr. James Deeds, Oncology Research Novartis • Dr. Rudi Bao, Oncology Research Novartis • Dr. Humphrey Gardner, TBG, BMD Novartis • Dr. Alokesh Duttaroy, DMDA Novartis • Dr. Steve Potts, Aperio • Dr. Reginald Valdez, Novartis • Dr Oliver Turner, Novartis • Many Others Image Analysis in Pathology

  38. Frank VoelkerDVM  MS Diplomate ACVPKey bio points / specialties Pathology Experts LLC provides its sponsors with the highest level of expertise in toxicologic pathology with focus on adding value to the preclinical phase of drug and device development. We provide a wide range of consulting services across all major therapeutic areas and organ systems. Contact us @ www.pathexperts.com Basel Switzerland – Rye, New York USA Image Analysis in Pathology

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