Ivan De Mitri * on behalf of MAGIC-5 collaboration *Dipartimento di Fisica dell’Università del Salento and INFN, Lecce, Italy [email protected] 12th

Ivan De Mitri *on behalf of MAGIC-5 collaboration

*Dipartimento di Fisica dell’Università del Salento and INFN,Lecce, Italy

[email protected]

12th International Conference on Applied Stochastic Models and Data Analysis Chania, Crete, Greece, May 29- June 1, 2007

Implementing Computer Assisted Detectionn systems for the analysis of mammograms, lung CT scans, and brain PET

and NMR images

The Magic-5 Project 2Ivan De Mitri

The medical applications of the MAGIC-5 project cover at present three main fields:

1. breast cancer detection in mammograms

2. nodule detection in lung CT images

3. the diagnosis of the Alzheimer disease (AD)

…by using also the GRID !

MAGIC–5Medical Applications on a Grid Infrastructure Connection


MAGIC–5Medical Applications on a Grid Infrastructure Connection

A collaboration of severalUniversities,

Local INFN Sectionand Hospitals

International CollaborationsCentro de Applicaciones Tecnologicas y

Desarrollo Nuclear (CEADEN) , CubaALICE collaboration – CERN Ginevra

Collaborations with IndustriesBRACCO Imaging, EURIX, I&T


CAD Station for MammographyMassive Lesion Microcalcifications

• Image Selection• Image manipulation• Metadata insertion• Diagnosis insertion• CAD execution• Data Registration• Data Search

• Installations Hospitals: Valdese (TO) Palermo Lecce INFN-Universities: Bari, Lecce, Napoli, Palermo,Torino, Sassari


Circularity Inertial Momentum

Mean Radial Length Mean Intensity

STD of theRadial Length STD of the Intensity

Entropy of the intensity distribution

Anisotropy

Fractal index Area

Eccentricity …………………………

CAD for mammography: Some of the used features

ji,

ijPixelEnergy )ijji,

ij log(PixelPixelEntropy



Density Comparison

A Code for the scale normalisation was developed based on the overlap of the area outside the breast

Before Treatment After Treatment

Density measurement at different times will allow the patient monitoring during different types of therapy

Breast range starts here Breast range starts here


Nodule detection in lung CT scansTwo steps already implemented

1. automated extraction of the pulmonary parenchyma;

2. detection of nodule candidates based on several independent methods

Ivan De Mitri

The Nodule Topology

n 1:internal n 3:pleuraln 2:sub-pleural


First Threshold identification(Intensity histogram on a central slice)

First Threshold identification(Intensity histogram on a central slice)

3D Region Growing 3D Airways segmentation

3D Region Growing 3D Airways segmentation

Cranio-caudal Sorting of images in the dicomdir

Cranio-caudal Sorting of images in the dicomdir

…

Lung CAD: One of the approaches


Wavefront algorithmBronchial tree segmentation

Wavefront algorithmBronchial tree segmentation

Threshold adjusting(avoid lungs fusions, etc.)

Threshold adjusting(avoid lungs fusions, etc.)

Authomatic trachea identificationAuthomatic trachea identification

…


ROI Hunter 3D ROI Hunter 3D

False positive filteringFalse positive filtering

…

slice z

slice z+1

slice z-1





Results from CAD for lung CT (for one of the implemented algorithms)

4 . M.S. Brown, J.G. Goldin, S. Rogers, H.J. Kim, R.D. Suh, M.F. McNitt-Gray, S.K. Shah, D. Truong, K. Brown, J.W. Sayre, D.W. Gjertson, P. Batra, and D.R. Aberle, “Computer-aided Lung Nodule Detection in CT: Results of Large- Scale Observer Test”, Academic Radiology 12 (6), 681-686 (2005).

6. K. Suzuki, S.G. Armato III, F. Li, S. Sone, and K. Doi, “Massive training arti- ficial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography”, Medical Physics 30 (7), 1602-1617 (2003).

7. M.N. Gurcan, B. Sahiner, N. Petrick, H.-P. Chan, E.A. Kazerooni, P.N. Cas- cade, and L. Hadjiiski, “Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system”, Medical Physics 29 (11), 2552-2558 (2002).

8. Y. Lee, T. Hara, H. Fujita, S. Itoh, and T. Ishigaki, “Automated Detection of Pulmonary Nodules in Helical CT Images Based on an Improved Template- Matching Technique”, IEEE Transaction on Medical Imaging, Vol. 20, No. 7, 595-604 (2001).

9. 9 A.S. Roy, S.G. Armato III, A. Wilson, K. and Drukker, “Automated detection of lung nodules in ct scans: False positives reduction with the radial-

gradient index”, Medical Physics 33 (4), 11331140 (2006).


The case of the Alzheimer deseaseThe quantitative comparison, through the SPM (Statistical

Parametric Mapping) software, of PET images

from suspected AD patients with images of “normal” cases, allows powerful

suggestions to an early AD diagnosis.

The use of an integrated GRID environment for the

remote and distributed processing of the PET

images at a large scale, is strongly desirable.

This application is implemented in the MAGIC-5

GRID infrastructure.


Use both NMR and PET images


Rat

e of

Atr

ophy

(m

m3 /

yr)

Controls AD

0.5 1 1.5 2 2.5 30

0.5

1

1.5

Observable value

Em

piric

al pdf

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

False Positive (1-Specificity)

Tru

e P

ositiv

e (

Sensitiv

ity)

sN = 93 %

NormalAD

ROC Area 93%

First results are encouraging


Summary CAD for mammography• Several working prototypes are being installed and tested in different accademic sites and hospitals

• Upcoming participations to real screening programs

CAD for lung CT scans• Different approaches gave promising results in terms of both sensitivity and false positive fraction

• Upcoming test on large scale databases

Early diagnosis of AD• Good preliminary results obtained from the hippocampus segmentation

• Tests are under way to combine information from different diagnosis tools (NMR, PET, ..)

Documents

Ivan De Mitri * on behalf of MAGIC-5 collaboration *Dipartimento di Fisica dell’Università del Salento and INFN, Lecce, Italy [email protected] 12th