CC Workshop

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MAGIC-5

• Medical Applications on a
• Grid Infrastructure Connection

INFN Bari, Cagliari, Catania, Lecce, Napoli,
Pisa, Torino Universities Bari, Genova, Lecce,
Napoli, Palermo, Piemonte Orientale, Pisa,
Sassari Hospitals Alessandria, Bari, Livorno,
Milano, Napoli, Palermo, Pisa, Sassari,
Torino, Udine
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CALMA

• Breast Cancer Screening
• Increased survival rate
• Problems costs and manpower
• Computer Assisted Detection

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CALMA Results

• Largest Database of digitised mammograms ( gt
  5000)
• ROC (Receiver Operating Characteristic) Curve

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2001 CALMA Open Issues

• Virtually unlimited Database size
• Intrinsically distributed Database many sources
• Network connections
• Access required to all the images

• Use Cases
• Large Scale Screening
• Teleradiology diagnosis training
• CAD on demand

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Medical Imaging communities

• Medical
• (distributed application use case)
• Distributed databases, owned resources
• Special security needs privacy
• Ease of installation, maintenance and access
• Small, single-purpose, single-VO dedicated grids
• An example the GPCALMA project

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GPCALMA Screening
CAD selection to minimize data transfers
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GPCALMA Tele-training Epidemiology
3 - Spawn Processes
2 - Start CAD
4 - Remote Analysis
5 - Retrieve Analyze Selected Images
1 - Data Selection
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GPCALMA CAD on demand
5 - Run CAD algorithm
3 - Ask for CAD
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GPCALMA
How to implement the above described Use Cases?

• Move code rather than data
• Share the images without moving them

• Single VO in hospitals
• Secure Access
• Distributed Data Management
• Scheduling of Computing Resources

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The GPCALMA Graphic User Interface
In use Bari Napoli Pisa Sassari Torino
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The GPCALMA distributed system configuration

• Clients installed Lecce, Napoli, Pisa, Sassari,
  Torino

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The AliEn-GPCALMA Core Serviceshttp//gpcalma.to.
infn.it
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Catalogue query
Patient creation
Image registration
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GPCALMA Achievements

• Ongoing tasks
• C ROOT-AliEn API for Input Data Selection
• improve the algorithms performance new
  approaches
• optimise the implementation of data and metadata
• set up a prototype in the participating hospitals

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GPCALMA CAD News

• Masses
• ROI Search
• Features
• AREA
• Perimeter/AREA
• Entropy
• Fractal Dimension
• Neural Network

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GPCALMA CAD News

• Microcalcifications

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GPCALMA CAD News

• Microcalcifications
• Pre-Processing
• Features
• AREA
• Perimeter/AREA
• Neural Network
• Classification negative,
• benign, malignant

Number of samples Number of samples NN not reached False Clusters Benign Malignant
False Clusters 29 6 19 4 0
Benign 5 0 1 4 0
Malignant 8 0 0 1 7
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GPCALMA from GENIUS
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GPCALMA on iBook
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MAGIC-5

• INFN expertise and leadership in
• CAD development
• Grid Middleware
• Does any other Medical field but mammography
  require a similar approach?
• CAD for Lung Cancer detection its on time
  like CALMA!
• 3D CT images
• search for different patterns
• same Grid approach
• AliEn is presently the best available Grid
  implementation in terms of
• easiness of installation, functionality,
  stability and scalability
• Alzheimers disease diagnosis
• Colonoscopy (?)
• MAGIC-5
• 1 project (MAGIC-5) and common GRID Services
• 3 Virtual Organisations
• GPCALMA
• ANPI (Analisi Neoplasie Polmonari in Italia)
• ADD (Alzheimers Disease Diagnosis)

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CAD for Lung Cancer?

• 5 years survival rate for lung cancer 14 (US),
  10-15 (EU)
• no improvement in the past 20 years
• Low dose CT 6 times more efficient than Chext
  X-Ray (CXR) in the detection of state I malignant
  nodules
• CAD methods are being explored
• Gurcan et al., Med. Phys. 29(11), Nov. 2002,
  2552 computerized detection for lung nodules
  in helical CT images is promisinglarge
  variations in performance, indicating that the
  computer vision techniques in this area have not
  been fully developed. Continued effort will be
  required to bring the performances of these
  computerized detection systems to a level
  acceptable for clinical implementation.

About 43 images/patient About 0.5 MB/image
Number of cases Sensitivity () FP/image Authors
17 95.7 0.3 Fiebich
17 72 4.6 Armato
26 30 6.3 Fiebich
43 71 1.5 Armato
16 86 2.3 Ko
34 84 1.74 Gurcan
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Spiral CT imaging principles

• Linear patient motion through the gantry
• Beam rotation
• spiral pattern of data acquisition
• one continuous set of volume data
• Reconstruction options
• (Slice reconstruction increment)
• (Interpolation algorithm)
• (Effective slice thickness)

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Multi-slice vs. Single-slice

• Volume Coverage

N number of DAQ channels 4 P pitch
(linear movement in T/beam collimation) S
detector width (mm) T execution time (s) R
rotation time (s) 0.5 s
mAs kV Collimation (mm) Pitch T (s) Step (mm)
SSCT 43 140 3-5 21 1 1
MSCT 20 120 1(x4) 71 0.5 2-5
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Images an example
5 mm 140 KV 120 mAs
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Screening in Italy EU-US

• Main goal
• reduce the death rate caused by lung cancer
• The sample
• 55-69y
• gt20 (packs/day) y
• Smokers (or ex-smokers lt 10 y)
• Agreement
• No previous cancer
• Italy
• Ongoing programs Genova, Milano, Torino
• Starting phase Regione Toscana Emilia-Romagna
• About 7000 exams in 4 years
• EU US
• Collaborative Spiral CT-group
• I-ELCAP International Early Lung Cancer Action
  Project
• EU ELCDG EU Early Lung Cancer Detection Group
• US National Lung Screening Trial (50,000 people)

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Neuroinformatics Portal

• Interface for GRID applications
• Statistical analysis of PET images databases for
  the study of the Alzheimer Disease
• Alzheimer Disease (AD) is the leading cause of
  dementia, accounting for more than half of all
  dementias in elderly people
• Why Grid?
• Highly difficult collection of a control group
  built with normal images
• Remote access to a database of normal patients
• Access control (Cfr registration, autentication,
  certification)
• Interactive SPM Statistical Analysis

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The Alzheimer Diagnosis Use Case Univ. Ge, MiB,
Osp. S. Raffaele
SET of CONTROLS 1 (PET, SPECT IMAGES)
SET of CONTROLS 2 (PET, SPECT IMAGES)
STATISTICAL TOOL (SPM)
UP LOAD
SET of CONTROLS 3 (PET, SPECT IMAGES)
IMAGE of PATHOLOGIC SUBJECT (PET or SPECT IMAGE)
STATISTICAL ANALYSIS OF THE UPLOADED IMAGE
SET of CONTROLS n (PET, SPECT IMAGES)
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Alzheimer Disease Use Case
Server Node
User Node
SPM Client Data Collection
SPM Client Data Collection
User Node
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Alzheimer Disease Use Case
User Node
Image Acquisition Reference Atlas
Selection Image Transfer Maps Visualisation
1
4
Repository Node
Server Node
Image Normalisation Data catalogue Query Image
Transfer Statistical Analysis Maps Transfer
Image Normalisation Image Comparison Results
Transfer
Repository Node
Image Normalisation Image Comparison Results
Transfer
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2
3
3
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Conclusions

• Breast Cancer Detection in Screening Programs
  good example of e-health application that would
  benefit from the use of GRID Services
• The AliEn/PROOF based approach allows
• Minimisation of data transfers
• Secure management of a distributed Virtual
  Organisation
• The success will depend on
• the reliability and stability of interactive GRID
  Services
• the performance of CAD algorithms ongoing new
  approaches
• the quality of the GUI
• GPCALMA Virtual Organisation in the participating
  Hospitals
• by the end of 2004 with improved CAD algorithms
• New applications will follow
• ANPI, ADD, COLON
• EGEE/LCG/ARDA Architecture Roadmap towards
  Distributed Analysis
• Prototype developed in the framework of EGEE by
  Sep 2004
• Migrate to that prototype

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Blue VO
Green VO