Ivan De Mitri *on behalf of MAGIC-5 collaboration
*Dipartimento di Fisica dell’Università del Salento and INFN,Lecce, Italy
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
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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
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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
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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
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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
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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
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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
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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
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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
…
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ROI Hunter 3D ROI Hunter 3D
False positive filteringFalse positive filtering
…
slice z
slice z+1
slice z-1
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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 tomog- raphy 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).
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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.
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Use both NMR and PET images
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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
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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, ..)