Practical Image Analysis from a Pathologist’s Perspective

Frank A. Voelker, DVM, DACVPPathology Experts LLC

Practical Image Analysis from a Pathologist’s Perspective

2/4/2008Image Analysis in Pathology 2

Topics…….

Introduction

General Concepts and Approaches

Guidelines and Pitfalls

Analytical Strategies

Applications and using Genie™

Summary


General Analytical Approaches…….

Area Based Analysis

Rare Event AnalysisCell Based Analysis

Pixel CountIHC DeconvolutionCo-localization

Rare EventIHC NuclearMembraneAngiogenesis


Two Different Approaches for Analysis

Cellular Hypertrophy/Atrophy

Cell Numbers

Tissue Infiltrates (eg. Fibrosis)

Other Structural Alterations

Cellular Hypertrophy/Atrophy

Cell Numbers

Tissue Infiltrates (eg. Fibrosis)

Other Structural Alterations

Histochemistry

IHC

ISH

Histochemistry

IHC

ISH

Quantify Substances using Special Stains

Usually measuring area or number

Usually measuring area and/or intensity

Quantify Histomorphologic Change


pS6 Ser235 Immunostain of Breast Carcinoma

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.

Introducing the Concept of “Targeted Cell” Analysis


Fibrosis in Livers of Zucker Rats

Control Rat No. 12 Fenofibrate Rat No. 5

Pioglitazone Rat No. 3

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

C

Compound X Rat No 2

T

T

T

T

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0.5

1

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C F P X

Use of the Positive Pixel Count Tool enables “visually apparent” analysis of a change


Quantitation of PAS Stain for Glycogen in Livers of DIO Mice Administered XXX Using the Aperio Color Deconvolution Tool

PAS-stained Section Aperio Markup Image

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5

10

15

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1 2 3

Using the Color Deconvolution Tool enables quantitation of things visually obscured by counterstaining

8 Image Analysis in DD / Voelker / 09/12/06

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)


Mouse Liver - Hepatocellular Hypertrophy

Total Hepatocyte Nuclei = 167 Average Nuclear Size = 160 µm² 508 nuclei/mm²

Total Hepatocyte Nuclei = 199 Average Nuclear Size =140 µm² 706 nuclei/mm²

Algorithm: IHC Nuclear (cell-based)

Drug-related enzyme induction leading to increases in cytoplasmic endoplasmic reticulum with resultant hepatocyte size increase.


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.

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.


Anatomic Consistency in Sampling…..


Sirius Red Stain Depicting Myocardial Fibrosis in a Mouse

Precision in level of section is required for accurately comparing amounts of fibrosis between treatment groups

Analysis Tool: Color Deconvolution (area-based)


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.


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

Mouse Livers

263 nuclei/mm²

212 nuclei/mm²


Three Possible Strategies for Measuring Brown Stains using the Positive Pixel Count Analysis Tool

1. Quantitate the percentage area of all brown pixels in the section or in selected areas of the section.

2. If the chromagen staining is very extensive in the target cell population, measure only the brownest (darker) pixels in selected areas of the section.

3. If the chromagen staining is uniform in character and very extensive in the target cell population, measure stain intensity as an index of concentration.

1. Quantitate the percentage area of all brown pixels in the section or in selected areas of the section.

2. If the chromagen staining is very extensive in the target cell population, measure only the brownest (darker) pixels in selected areas of the section.

3. 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

* p .01 **p .001

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1 2 3 4

*

**

Control 100 mg/kg

250 mg/kg1000 mg/kg

%

Measuring all of the brown pixels in the sample area

18 Aperio in TBD / Voelker / 08/24/06

Quantitation of Cytochrome p450 Reductase in Centrilobular Hepatocytes Despite Widespread Immunostaining

Original Image Markup Image

Measuring only the area of more intense stainColor deconvolution (area-based)


Quantitation of VEGF Immunostaining in Livers of Mice administered XXX for 52 Weeks

44.00

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Control Males

Control Females

1000 mg/kg Males

1000 mg/kg FemalesComparing stain intensity


Variable staining of neoplasm and staining of surrounding stroma make morphometric analysis difficult.

PTEN Immunostain of Squamous Cell Carcinoma in Human Lung

The Challenge of Analyzing only the Target Tissue…….


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.


Genie™……..

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

Strategies


Steps in Chromagen Analysis of a Neoplasm (Excluding the Stroma)

Primary IHC image Genie™markup with selection of neoplasm

Eliminate stroma Final Aperio ImageScope deconvolution markup

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Quantitation of Splenic Extramedullary Hematopoiesis in a Mouse using Genie™ and the Aperio Positive Pixel Count Tool

H&E Stain

Genie™ Markup Image

Positive Pixel Markup Image

Results: EMH comprises 50.2% positive pixels in evaluation area


Quantitation of Periarteriolar Lymphoid Tissue in a Mouse Spleen using Genie and the Aperio Positive Pixel Count Tool

Aperio Positive Pixel Markup Image

H&E Stain

Genie 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.


Analysis of Study Sample Groups by Genie™

Targeted Tissue Selection and Isolation by Genie™

Subsequent Uniform Analysis of Isolated Target Tissue for area/intensity

Morphologically Variable Samples Trained Individually for Genie Target Tissue Selection

Separate target tissue training of each sample does not adversely affect final analysis.


Bile Duct Hyperplasia in Rat Liver

Hyperplastic Bile Ducts = GreenHepatic Parenchyma = RedPeriportal Inflammatory Cells = BluePeriductal Collagen = BrownBile Duct Lumena + Sinusoids = Yellow

First pass Genie histology pattern identification with minimal training. Genie™ can simultaneously analyze three or more tissue areas

Then analyze up to three tissue areas using colocalization tool


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


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.


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


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


Islet Cell Mass of Mouse Pancreas

Measurement of Pancreatic Islet Cell Mass using Genie™ Followed by the Colocalization Algorithm

(A/B)C=Islet Cell MassA=Total Islet Area in Section

B=Total Pancreas Area in SectionC=Pancreatic Weight


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


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.


Measuring Cellular Hypertrophy of two cell types in a Dog Thyroid Gland

Then apply IHC nuclear tool on same image to get numbers of artificially colored brown and blue nuclei.

Set Genie™ masks for brown follicular epithelium and blue c-cells.

Then apply colocalization tool to calculate respective areas of each.

Total Brown Area/Total Brown Nuclei = Mean Follicular Cell Area. Do same calculation for blue nuclei.


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.


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


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.comBasel Switzerland – Rye, New York USA

http://www.pathexperts.com/

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Practical Image Analysis from a Pathologist’s Perspective