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Pathology Visions: Approaches to Tissue-based Image Analysis in Pharmaceutical Research and Development PowerPoint PPT Presentation

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Pathology Visions: Approaches to Tissue-based Image Analysis in Pharmaceutical Research and Development . Frank A. Voelker, DVM, DACVP Flagship Biosciences. Topics……. Introduction Applications and Challenges Concepts and Approaches Analytical Strategies Guidelines and Pitfalls

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Pathology Visions: Approaches to Tissue-based Image Analysis in Pharmaceutical Research and Development

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Pathology Visions: Approaches to Tissue-based Image Analysis in Pharmaceutical Research and Development

Frank A. Voelker, DVM, DACVP

Flagship Biosciences


  • Introduction

  • Applications and Challenges

  • Concepts and Approaches

  • Analytical Strategies

  • Guidelines and Pitfalls

  • Various Examples Using Genie™

  • Summary

Pharmaceutical Research and Development

Oncology Clinical Trial

  • Samples usually morphologically variable

  • Intragroup collection conditions variable

  • Target tissue limited to neoplasm, stroma

Early Drug Discovery

  • Samples usually morphologically similar

  • Intragroup collection conditions adjusted similar

  • Target tissue variable depending on project

  • Samples usually morphologically similar

  • Intragroup collection conditions adjusted similar

  • Target tissue variable depending on project

Preclinical Safety Testing

Different approaches for each setting are required because of different group sizes, tissue types and tissue heterogeneity. However, segregation of target from nontarget tissue during analysis is a major challenge in all settings

Available Analytical Tools…….

Pixel Count

IHC Deconvolution


IHC Nuclear

Rare Event



pS6 Ser235 Immunostain of Breast Carcinoma

Introducing the Concept of “Target Tissue” 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.

Analysis of Target Tissue

In it’s Simplest Terms…..

  • Count and measure simple structures/objects.

  • Measure area of defined regions/stain.

  • Measure intensities of stain as a percentage of defined regions.

  • Combinations of 1, 2 and 3 above.

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

Morphologic Approach……

Quantifying Common Microscopic ToxPath Changes using H&E or Special Stains

  • Liver: Hepatocellular hypertrophy, bile duct hyperplasia, necrosis, acute and chronic inflammation, extramedullary hematopoiesis, periportal fibrosis, fatty change, glycogen accumulation.

  • Kidney: Tubular basophilia, hyaline droplet degeneration, casts, tubular necrosis.

  • Spleen: Lymphoid hyperplasia/atrophy, extramedullary hematopoiesis

  • Lung: Alveolar edema, pneumonia, congestion.

  • Heart: myocardial fibrosis.

  • Adrenal gland: cortical hypertrophy, cortical vacuolation.

  • Skin: Acute and chronic inflammation, acanthosis

Biggest Problem: Distinguishing target from nontarget tissue

Methods for Defining the Target Tissue for Analysis

  • Define the target tissues for analysis by existing algorithms using common (eg H&E) or special (eg IHC) staining procedures.

  • Define the target tissues for analysis using Genie™

  • Assist in defining target tissues in 1 and 2 above by using the positive and negative pen tools.

A high degree of accuracy in target tissue definition will assure a high degree of accuracy in the final analysis.

Mouse Liver - Hepatocellular Hypertrophy

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)

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.

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)

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

Fibrosis in Livers of Zucker Rats



Fenofibrate Rat No. 5

Control Rat No. 12




Pioglitazone Rat No. 3

Compound X Rat No 2





Variations in fibrosis (blue) about small portal triad veins (T) as depicted using Masson’s Trichrome stain

Quantitation of PAS Stain for Glycogen in Livers of DIO Mice Administered XXX Using the Aperio Image Analysis System

Analysis Tool: Color Deconvolution (area-based)

PAS-stained Section

Aperio Markup Image

Three Possible Strategies for Measuring Immunohistochemistry 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.

Percent of Liver Tissue Staining for Transferrin Receptor(CD71) in Female Mice by Immunohistochemistry

Measuring all of the brown pixels in the sample area





100 mg/kg

250 mg/kg

1000 mg/kg

* p  .01 **p  .001

Cytochrome p450 Reductase Immunostaining of Centrilobular Hepatocytes

Widespread staining with centrilobular distribution of more intense staining

Quantitation of Cytochrome p450 Reductase Immunostaining of Centrilobular Hepatocytes by Aperio

Original Image

Markup Image

Measuring only the area of more intense stain

Color deconvolution (area-based)

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


Introducing the concept of using histology pattern recognition software as a preprocessing machine to segregate target from nontarget tissue during analysis


Tumor Cell-Specific Biomarker Analysis using Genie Histology Pattern Recognition Software

Genie mark-up image. Tumor cells = blue

Pulmonary adenocarcinoma stained for pS6-Ser240

IHC nuclear analysis of tumor cells

Positive pixel count analysis of tumor cells

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 affect final target tissue analysis.

Immunostain Analysis of Human Breast Tumor Tissue Micro Arrays

Multiple Genie™ Training Classifiers may be needed in analysis of a TMA slide because of tumor heterogeneity.

Tumor Cell-Specific Biomarker Analysis of TMA Breast Tumor Samples using Genie Histology Pattern Recognition Software


Genie Mark-up

Positive Pixel Mark-up

Manual Inclusion/Exclusion as a Method of Defining the Target Tissue

Use of the Positive and Negative Pen Tools……

  • Usually done when it is difficult to define target tissue components in the image by stain specificity and/or by pattern recognition software.

  • May be laborious and time-consuming to perform manual inclusion or exclusion especially with complex tissue patterns.

  • Potentially introduces subjective evaluation by the operator which may further increase error.

  • However, may be the easiest and most rapid method of helping to define the target tissue.

Use of Positive and Negative Pen Tools

Similar IHC staining of fibronectin and secretion droplets in this xenograft tumor with subsequent poor differentiation by the Genie™ classifier required the use of the negative pen tool to assist in quantitating fibronectin using the IHC deconvolution algorithm.

“H Score”: A Convention for the Simultaneous Assessment of Both Area and Intensity of Stain

pS6 Ser235 Immunostain of Squamous Cell Carcinoma in Human Lung

Estimation of Average stain intensity should take into account negative-staining regions of target tissue as well as positive-staining regions

Scoring is a Convention for Determining Average Stain Intensity of Target Tissue

  • With the old subjective scoring method, the pathologist visually scored staining features of cells (eg. cytoplasmic, nuclear, or membranous staining) by intensity of stain according to grades 0, 1+ , 2+ or 3+ using the following formula:

  • (1)x(%1+)x(%Area) + (2)x(%2+) x (%Area) + (3)x(%3+)x(%Area) = “H” Score

(For a maximum of 300)

Now “H” Score evaluation is automatically calculated in Aperio’s IHC Deconvolution Algorithm using attribute outputs in the following similar formula:

(Nwp/Ntotal)x(100) + Np/Ntotal)x(200) + Nsp/Ntotal)x(300) = “H” Score


Nwp = Number of weakly positive pixels

Np = Number of moderately positive pixels

Nsp = Number of strongly positive pixels

Ntotal = Total number negative + positive pixels

Not available with IHC Nuclear and Membrane Algorithms

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.

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 Liver

Consistency of Necropsy Conditions Can Affect Morphometric Analysis

Variations in exsanguination during necropsy may result in differences in sinusoidal dilatation thus leading to inaccurate analysis

Mouse Liver

Consistency of Sample Area Selection for Morphometric Analysis within the Median Lobe of the Mouse Liver




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.

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.

Quantitation of Splenic Erythropoiesis in a Mouse using Genie™ and Aperio Algorithms

Original H&E Stain

Genie™EMH Classifier

Measuring Erythroid Nuclear Area Using Hematoxylin Channel of Deconvolution Algorithm

Counting Erythroid Nuclei Using Nuclear Algorithm

Quantitation of Hepatocellular Necrosis

Use of Genie™ as a preprocessing utility to identify regions of hepatic necrosis (red) and areas of normal liver (grey)

Subsequent quantitation of necrotic area to allow precise grading

Using the Microvessel Analysis Algorithm to Assess Angiogenesis in a Xenograft Neoplasm

Use of the microvessel analysis algorithm to assess angiogenesis in a xenograft neoplasm in a mouse

Microvessel analysis provides important information regarding potential antineoplastic effects of pharmaceutical compounds

Using the Microvessel Analysis Algorithm to Count Cells

Use of the microvessel analysis algorithm to assess macrophage populations in mouse xenograft neoplasms

Threshold algorithm parameters are modified to accommodate the smaller size and shape characteristics of the macrophages

Using the Microvessel Analysis Algorithm to Count Cells

Algorithm Output Provides Valuable Cell Population Data

  • Total Number of Cells

  • Total Area of Analysis

  • Total Cell Area

  • Cell Density/Unit Area

  • Average Stain Density

  • Mean Cell Area

  • Histogram of Cell Areas

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 as part of the Genie™ utility to determine area and relative percentage of each separate tissue component.

Measuring Follicular Cell Hypertrophy in Dog Thyroid Gland

Use of Genie™ to segregate the thyroid gland follicular epithelium (green) as an intended target tissue for analysis

Then apply the nuclear algorithm counting total nuclear numbers in the target tissue.

Total Nuclei/Total Target Tissue Area = Mean Follicular Cell Area

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

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

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


  • 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.


  • Ms. Kimberly Merriam, TBG, BMD Novartis Pharmaceuticals

  • Ms. Jeanette Rheinhardt, TBG, BMD Novartis Pharmaceuticals

  • Dr. Allen Olson, Aperio Technologies, Inc.

  • Dr. Kate Lillard-Wetherell, Aperio Technologies, Inc.

  • Mr. James Deeds, Oncology Research, Novartis Pharmaceuticals

  • Ms. Veronica Travaglione, Pharmacology, Infinity Pharmaceuticals

  • Mr. Igor Deyneko, Pharmacology, Infinity Pharmaceuticals

  • Dr. Humphrey Gardner, TBG, BMD, Novartis Pharmaceuticals

  • Dr. Steve Potts, Aperio Technologies, Inc

  • Dr. Reginald Valdez, Novartis Pharmaceuticals

  • Dr Oliver Turner, Novartis Pharmaceuticals

  • Mr. Trevor Johnson, Aperio Technologies, Inc.

  • Others

Frank VoelkerDVM  MS Diplomate ACVPKey bio points / specialties

Flagship Biosciences LLC provides biotech, pharmaceutical, and medical device companies with quantitative pathology services.

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