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Raul Ramos – Exploiting Grid Infrastructures for Medical Image Analysis

DSIE ‘10 DOCTORAL SYMPOSIUM ON INFORMATICS ENGINEERING. Porto, Jan 2010

Exploiting Grid Infrastructures for Medical Image Analysis

Exploiting eInfrastructures for Medical

Image Storage and Analysis: A Grid

Application for Mammography CAD

Raul Ramos / CETA-CIEMAT

Miguel Angel Guevara / INEGI-FEUP

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OUR WORK

INTRO TO CADMAMMOGRAPHY

Outline

1. Mammography

2. CAD systems and techniques

3. Validation and relevance

4. Current Status and Issues

5. Motivation and goals

6. Framework description

7. Results

8. On going and future work

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1 Mammography

Mammography is of major impact of woman

health

• Improve survival rates, reduce health costs

• Improving accuracy (TP, FP, FN, TN)

Digital mammography

•SFM: Screen Field Mammography

•FDM: Full Digital Mammography

•Different image properties.

•Each better for different scenarios

IT Systems: PACS + DICOM

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1 Mammography

Target lessions (BIRADS atlas)

masses calcifications

architectural distorsions assimetries

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1 Mammography

BIRADS 2003 Assesment Categories

• Category 0: Needs additional evaluation and/or mammograms

• Category 1: Negative

• Category 2: Benign Finding

• Category 3: Probably Benign Finding – Initial short interval

follow-up suggested

• Category 4: Suspisious Abnormality – Biopsy should be

considered

• Category 5: Highly Suggestive of Malignancy – Appropriate

action should be taken

• Category 6: Known Biopsy – Proven Malignancy

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2 CAD Systems and ML Techniques

CAD on the literature (with some ambiguity)

• CADe Computer Aided Detection: Finds locations

• CADx Computer Aided Diagnosis: Characterizes a designated region

Second Opinion to ASSIST specialists

Approaches used in literature

• Pixel based

• Feature classification

Base technologies

• ML Classifiers

• Image Processing

• Statistical, etc.

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2 CAD Systems and technologies

Feature classification

ROI SELECTION

SEGMENTATIONFEATURE

EXTRACTIONCLASSIFICATION

IMAGE ENHANCEMENT

img processing

algorithms

(pixel/bit based)

dynamic contours,

semiautomatic

principal

component

analysis

ANNs, GAs, SVMsGUI based,

image recognition

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3 Validation and result comparison

Datasets are used to train

and/or validate systems

cross-validation, test/train

split for training and

generalization measure

Sensitivity vs specificity

ROC/FROC Curves

Metrics:

• TEST %, Az, TP/FP RATE

TEST% @ FP/IMAGE

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4 Current status and issues

Many systems in literature

• Real work started in late 80’s

• Feature classification: Mostly by FFBP, SOM and SVMs

• Microcalcifications >90% TP

• Other lesions 60-90%

Datasets are VERY HARD to build

• Medical institutions VERY sensitive to data privacy and availability

• Only two publicly available data sets:

- Mammographic Image Analysis Society (MIAS) DB. UK. 300 cases

- Univ South Florida Digital Database for Screening Mammography. 3000 cases

• Small data sets for training classifiers or validating data

• Sensible to doctor’s mood and population specificities

R2 Image Checker

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4 Current status and issues

Clinical acceptance

• A few works on medical workflow integration performace improvement

• Integration in hospital IT systems: PACs, Workstations, etc.

• CAD increases detection rate equivalently to second reading (3%-7%)

BASELINE OF TECHNOLOGIES AND METHODOLOGY ESTABLISHED

NEED TO INCREASE THE SIGNIFICANCE OF RESULTS

NEED TO INCREASE ACCEPTANCE AMONG MEDICINE PROFESSIONALS (TRUST AND CULTURE)

eINFRASTRUCTURES ARE NOT USED AT ALL

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5 Goals and motivation

Our hypothesis:

• Grid features (federation and security) provide an adecquate

framwork to overcome the described issues

• We can effectively exploit computer power and federated

storage capacity of Grid infrastructures to:

1. Build better data sets (large federated storage)

2. Build better classifiers (large federate computing power)

Our goals:

1. Build a pilot Grid based system achieving results

comparable to literature (this work)

2. Use Grid federations to build large data sets

3. Use Grid federations to explore the search space of possible

classifiers (exploit computer power)

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2 Grid federations

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6 Our Framework:Digital Repositories Infrastructure

X.509 security authentication

VO Based authorization

Large digitalized content goes to

the Grid federation

Patient data in local DB

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6 Our Framework: scenario

Deploy the MIAS DB on a pilot DRI Grid

infrastructure

Have doctors marking and diagnosing images

(create datasets) using Grid based tools

Build feature classifiers upon created datasets

• FFBP ANN

• SOM ANN

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7 Results

100 cases from MIAS segmented by doctors with DRI

Classifier performance

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8 Conclusion and future work

We have

• reached a level of accuracy comparable to literature.

• validated the usage of Grid infrastructures.

• built an scalable Grid framework, suited to doctors workflows.

We are now ready to exploit the Grid for CAD

Future (and present)

• build a large db of annotated diagnosed mammograms at

Hospital San Joao (600 cases to date)

• massively explore a large diversity of classifiers

- Used about 10 CPU-months to train about 1000 classifiers on a 50

core cluster, used FFBP, RB, GA, SA to train ANNs

• we can systematically reproduce literature results

• define good exploration strategies

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Exploiting eInfrastructures for Medical Image Storage and Analysis: A Grid Application for Mammography CAD

THANKS!!!

QUESTIONS @ ANY TIME

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