AIIA Lab, Department of Informatics Aristotle University of Thessaloniki Z.Theodosiou, F.Raimondo, M.E.Garefalaki, G.Karayannopoulou, K.Lyroudia, I.Pitas,

AIIA Lab, Department of InformaticsAIIA Lab, Department of InformaticsAristotle University of ThessalonikiAristotle University of Thessaloniki

Z.Theodosiou, F.Raimondo, M.E.Garefalaki, G.Karayannopoulou, Z.Theodosiou, F.Raimondo, M.E.Garefalaki, G.Karayannopoulou, K.Lyroudia, I.Pitas, I.Kostopoulos K.Lyroudia, I.Pitas, I.Kostopoulos

FISH IMAGE ANALYSIS SYSTEM FISH IMAGE ANALYSIS SYSTEM FOR BREAST CANCER STUDIESFOR BREAST CANCER STUDIES

Artificial Intelligence & Information Artificial Intelligence & Information Analysis LaboratoryAnalysis Laboratory

Department of InformaticsDepartment of Informatics Aristotle University of Thessaloniki, Greece Aristotle University of Thessaloniki, Greece

EACDA 2005Pisa Italy 28-30 September 2005


IntroductionIntroduction The evaluation of fluorescent in situ hybridization The evaluation of fluorescent in situ hybridization (FISH) images is a method used to determine Her-(FISH) images is a method used to determine Her-2/neu status of breast samples.2/neu status of breast samples.

FISH technique allows the analysis and quantification FISH technique allows the analysis and quantification of specific abnormalities (gene amplifications).of specific abnormalities (gene amplifications).

DNA probes target the HER-2/neu gene and attach DNA probes target the HER-2/neu gene and attach themselves to their targetthemselves to their target sequence. This process is called hybridization.

The probes carry special fluorescent markers that emit light, when the probes bind to the HER-2 genes.


Problem StatementProblem Statement

FISH images:FISH images:The Her-2 probes are The Her-2 probes are visible as orange stained visible as orange stained spots. Probes for spots. Probes for centromere 17 (CEP-17), centromere 17 (CEP-17), are visible as green spots. are visible as green spots. The sections are The sections are counterstained with DAPI, counterstained with DAPI, providing a blue providing a blue background for nucleus background for nucleus body.body.


Problem Statement (2)Problem Statement (2)

FISH images conventional analysis:FISH images conventional analysis: The ratio of HER-2/neu over CEP 17 dots within each The ratio of HER-2/neu over CEP 17 dots within each

cell nucleus is scored and then averaged for a number cell nucleus is scored and then averaged for a number of ~60 nucleus.of ~60 nucleus.

A ratio of >=2.0 of HER-2/neu to CEP 17 copy number A ratio of >=2.0 of HER-2/neu to CEP 17 copy number denotes amplification.denotes amplification.

The manual counting of FISH images is difficult The manual counting of FISH images is difficult task.task.

Current analysis of FISH signals in practice is Current analysis of FISH signals in practice is performed in a semi-automated way.performed in a semi-automated way.


Problem Statement(3)Problem Statement(3)

Proposed methodProposed methodAn integrated system for the automated classification of An integrated system for the automated classification of FISH cases from breast carcinomas samples.FISH cases from breast carcinomas samples.

The system employs a two-stage algorithm for spot The system employs a two-stage algorithm for spot

detection and nuclei segmentation.detection and nuclei segmentation.

Combining results from multiple images taken from a slice Combining results from multiple images taken from a slice for overall classification, the FISH signals ratio per cell for overall classification, the FISH signals ratio per cell nucleus are measured and cases are classified as positive nucleus are measured and cases are classified as positive or negative.or negative.


Algorithm descriptionAlgorithm description

Spot detection steps:Spot detection steps: 1. A top-hat filtering for noise removal.1. A top-hat filtering for noise removal.2. 2. A binary threshold is then applied to the two outputs.A binary threshold is then applied to the two outputs.3. 3. A spot template is computed for each channel.A spot template is computed for each channel.4. Normalized cross correlation between top-hat output 4. Normalized cross correlation between top-hat output and spot channel template. and spot channel template.5. Channel intensity contrast measure is estimated as: 5. Channel intensity contrast measure is estimated as:


Algorithm Description (2)Algorithm Description (2)

Nuclei segmentation steps:Nuclei segmentation steps:

1.1. A nonlinearity correction stepA nonlinearity correction step is first is first performedperformed

2.2. Algorithm by Otsu is employed to determine Algorithm by Otsu is employed to determine the threshold for initial nuclei segmentationthe threshold for initial nuclei segmentation

3.3. Inter nuclei and nucleus body holes study: Inter nuclei and nucleus body holes study: Analysis of the percentage P of the perimeter Analysis of the percentage P of the perimeter pixels of a circle centered on every hole pixels of a circle centered on every hole centroid. P is much higher for inter nuclei centroid. P is much higher for inter nuclei holes than for the nucleus region holes. P holes than for the nucleus region holes. P varied in the range of 90% to 40% for holes of varied in the range of 90% to 40% for holes of the first type and second type respectively.the first type and second type respectively.


Algorithm Description (3)Algorithm Description (3)

Inter nuclei holes Nucleus body holesInter nuclei holes Nucleus body holes

The distance transform is applied to the binary imageThe distance transform is applied to the binary image H-dome maxima of the resulting image to reduce the H-dome maxima of the resulting image to reduce the

number of spurious local maxima. Marked watershed number of spurious local maxima. Marked watershed transform using h-dome as markers.transform using h-dome as markers.


System PresentationSystem Presentation The system is a module for the Volumetric The system is a module for the Volumetric

Image Processing, Analysis and Visualization Image Processing, Analysis and Visualization software package, Eikona3D for windows.software package, Eikona3D for windows.

The system is supported with a user interface The system is supported with a user interface , making it very practical and simple to use., making it very practical and simple to use.

Contains three functionsContains three functions1.1. Automated Ratio calculationAutomated Ratio calculation2.2. Semi-automated Ratio calculationSemi-automated Ratio calculation3.3. Manual Ratio calculationManual Ratio calculation


System Presentation(2)System Presentation(2)

Automated Ratio CalculationAutomated Ratio Calculation Images are loaded in a an input bufferImages are loaded in a an input buffer Selection of “Automated Ratio Selection of “Automated Ratio

Calculation” functionCalculation” function Implementation of the detection Implementation of the detection

algorithm for red and green spots algorithm for red and green spots respectivelyrespectively

Implementation of nuclei segmentation Implementation of nuclei segmentation algorithmalgorithm

The ratio and number of valid nuclei are The ratio and number of valid nuclei are presented in a dialog boxpresented in a dialog box



Semi-automated Ratio CalculationSemi-automated Ratio Calculation Images are loaded in a an input bufferImages are loaded in a an input buffer Selection of “Semi-Automated Ratio Selection of “Semi-Automated Ratio

Calculation” functionCalculation” function The detected spots and segmented The detected spots and segmented

nucleus are presented in input imagenucleus are presented in input image The user corrects the detection and The user corrects the detection and

segmentation segmentation The Ratio and number of valid The Ratio and number of valid

nucleus are presented in a dialog boxnucleus are presented in a dialog box



Semi-Automated Ratio Calculation exampleSemi-Automated Ratio Calculation example



Manual Ratio CalculationManual Ratio Calculation Images are loaded in a an input bufferImages are loaded in a an input buffer Selection of “Manual Ratio Selection of “Manual Ratio

Calculation”Calculation” The user identifies the nuclei and The user identifies the nuclei and

then the red and green spots per then the red and green spots per nucleus.nucleus.

The Ratio and the number of valid The Ratio and the number of valid nucleus are presented in dialog box. nucleus are presented in dialog box.


ResultsResults

Four patient cases, were employed to evaluate Four patient cases, were employed to evaluate the precision of the system.the precision of the system.

Two were classified by an expert as positive Two were classified by an expert as positive and the other two as negative.and the other two as negative.

Every case was consisted of 15 images.Every case was consisted of 15 images.

Ratio calculation and classification using the Ratio calculation and classification using the automated ratio calculation function of the automated ratio calculation function of the system.system.


Results(2)Results(2)

ClassificationClassification

By an expertBy an expertRatioRatio

Classification Classification By systemBy system

Case #1Case #1 NegativeNegative 1.19121.1912 NegativeNegative

Case #2Case #2 NegativeNegative 1.17741.1774 NegativeNegative

Case #3Case #3 PositivePositive 2.14882.1488 PositivePositive

Case #4Case #4 PositivePositive 2.15512.1551 PositivePositive


ConclusionsConclusions

We have developed a system for the automated We have developed a system for the automated evaluation of Her-2/status in breast samples by evaluation of Her-2/status in breast samples by FISH image analysis.FISH image analysis.

The developed system uses a two-stage The developed system uses a two-stage algorithm for spot detection and nuclei algorithm for spot detection and nuclei segmentation. The outputs of the two algorithms segmentation. The outputs of the two algorithms were merged for estimating the average were merged for estimating the average red/green ratio per cell nucleus.red/green ratio per cell nucleus.

It can be used for automated, semi-automated It can be used for automated, semi-automated and manual Ratio Calculationand manual Ratio Calculation


Conclusions(2)Conclusions(2)

The manual mode can speed up the manual diagnosis The manual mode can speed up the manual diagnosis (red/green spot and cell counting).(red/green spot and cell counting).

The manual mode can be used for getting ground truth The manual mode can be used for getting ground truth from FISH images by doctors in order to verify the from FISH images by doctors in order to verify the automatic results.automatic results.

The classification results are encouraging for The classification results are encouraging for the further testing of the system in clinical the further testing of the system in clinical trials.trials.


Future perspectivesFuture perspectives

Testing the developed software using more Testing the developed software using more clinical trials (FISH images).clinical trials (FISH images).

Joint research activities.Joint research activities.

Joint publications for FISH image analysis.Joint publications for FISH image analysis.


More InformationMore Information

The developed system will be available The developed system will be available from 15th of October 2005. from 15th of October 2005.

Ioannis Pitas: Ioannis Pitas: [email protected]@aiia.csd.auth.gr

Web site: Web site: http://poseidon.csd.auth.grhttp://poseidon.csd.auth.gr


Z.Theodosiou, F.Raimondo, M.E.Garefalaki, G.Karayannopoulou, Z.Theodosiou, F.Raimondo, M.E.Garefalaki, G.Karayannopoulou, K.Lyroudia, I.Pitas, I.Kostopoulos K.Lyroudia, I.Pitas, I.Kostopoulos

FISH IMAGE ANALYSIS SYSTEM FISH IMAGE ANALYSIS SYSTEM FOR BREAST CANCER STUDIESFOR BREAST CANCER STUDIES

Artificial Intelligence & Information Artificial Intelligence & Information Analysis LaboratoryAnalysis Laboratory

Department of InformaticsDepartment of Informatics Aristotle University of Thessaloniki, Greece Aristotle University of Thessaloniki, Greece

EACDA 2005Pisa Italy 28-30 September 2005

Documents

AIIA Lab, Department of Informatics Aristotle University of Thessaloniki Z.Theodosiou, F.Raimondo, M.E.Garefalaki, G.Karayannopoulou, K.Lyroudia, I.Pitas,