MAGIC5 meeting - Genova 2011/5/4-6
A CAD system for cerebral glioma and therapy follow-up
in DTI and FLAIR: status report
Giorgio De Nunzio1, Marina Donativi1, Gabriella Pastore2, Matteo Rucco3 , Antonella Castellano4, Andrea Falini4
1. Dept of Materials Science, Univ. of Salento, and INFN (National Institute of Nuclear Physics) (Lecce, Italy)
2. PO 'Vito Fazzi' - UOC Fisica Sanitaria, and Dept of Materials Science, University of Salento (Lecce, Italy)
3. School of Science and Technologies, University of Camerino (Italy)
4. Neuroradiology Unit and CERMAC, San Raffaele Scientific Institute (Milano, Italy)
Glioma
•Common primary brain tumors.
•Typical infiltrative growth pattern  glioma cells preferentially
infiltrate along white matter fibers.
•Conventional MRI cannot accurately localize microscopic glioma
infiltrations, therefore it does not always permit precise
delineation of tumor margins or tumor differentiation
from edema and/or treatment effects.
Diffusion Tensor Imaging (DTI); Isotropic and Anisotropic maps
Diffusion of
water molecules
2
Materials and methods
Glioma in MR-DTI
CAD System for
glioma
segmentation
(texture analysis)
Glioma structure
during follow-up
3
Training step
Once upon a time…
csvoi
svoi
rsvoi
IMAGE
ACQUISITION
ROI
CREATION
SLIDING
WINDOW
MAZDA
ANN
(learn)
PCA
SCATTER
PLOT
FEATURE
SELECTION
CAD
IMAGE
ACQUISITION
GUI
SLIDING
WINDOW
MAZDA
ROI
CREATION
ANN
(classification)
PCA
4
NOW!!!
Training step
csvoi
svoi
rsvoi
IMAGE
ACQUISITION
ANN
(learn)
PCA
LDA
ALL IN MATLAB
IMAGE
ACQUISITION
SLIDING
WINDOW
ROI
CREATION
69 FEATURES
FEATURE
SELECTION
APPLY FILTER
CAD
GUI
ENTIRE BRAIN
SLIDING
WINDOW
69 FEATURE
FEATURE
SELECTION
ROI
CREATION
ANN
(classification)
PCA
LDA
5
Fisher’s score vs feature
6
Work in progress…
PCA or LDA?
7
Why Principal Component Analysis?
Maximize variance by axis transformation
8
Dimensionality Reduction
Can ignore the components of lower significance.
25
15
10
5
0
PC1
PC2
PC3
PC4
PC5
PC6
PC7
PC8
PC9
PC10
Variance
Variance (%)
20
Dimensionality
You do lose some information, but if the eigenvalues are small, you
don’t lose much
–
–
–
–
n dimensions in original data
calculate n eigenvectors and eigenvalues
choose only the first p eigenvectors, based on their eigenvalues
final data set has only p dimensions
9
Limitations of PCA
Are the maximum-variance variables the relevant
features for discrimination preservation?
10
Linear Discriminant Analysis
• What is the goal of LDA?
− Perform dimensionality reduction “while preserving as much of the
class discriminatory information as possible”.
− Seeks to find directions along which the classes are best separated.
− Takes into consideration the within-class scatter but also the
between-class scatter.
11
Feature dimensionality reduction methods
PCA given an s-dimensional vector representation
(features) of each sample in a training set, Principal
Component Analysis (PCA) tries to find a s-dimensional
space whose basis vectors correspond to the
maximum-variance directions in the original feature
space. The dimensionality of this new space is then
normally decreased to a lower one (t << s) by
neglecting directions with low eigenvalues.
If x is the feature array, it is possible to diagonalise the
covariance matrix:
LDA Linear Discriminant Analysis finds the vectors in
the space that best discriminate among classes.
For two classes, the solution proposed by Fisher is to
maximize a function that represents the difference
between the means, normalized by a measure of the
within-class scatter
Between-class scatter matrix
and obtain the eigenvalues of the linear transformation
Matrix T, that is
Within-class scatter matrix
Starting from that, it is possible to calculate the PC’s
25
Variance (%)
20
15
10
5
0
PC1
PC2
PC3
PC4
PC5
PC6
PC7
PC8
PC9
PC10
12
PCA
LDA
13
PCA
LDA
14
… Classifier:Artificial Neural Networks (ANN)
FLAIR
5 patients for training and 4 for test
Back-propagation feed-forward ANN:
• 1 hidden layer, with 3 neurons
LDA
450
• 1 output neuron
400
350
300
250
200
150
100
PCA
LDA
50
0
-1.55
AUC=0.94
-1.5
-1.45
-1.4
AUC=0.97
15
… some results of map creation and segmentation
Probability maps in p, q or FLAIR images: the dots
mark the positions of the sliding window (svoi centers).
Color scale: darker colors for low probability values,
lighter colors for high values.
Red line: shows the segmentation produced by the CAD
system (“arbitrary” threshold)
16
… some results of map segmentation
P MAP
6 patients for training and 6 for test
P MAP - PCA
AUC=0.88
P MAP – MED ROI
P MAP - LDA
AUC=0.95
17
… some results of map segmentation
Q MAP
6 patients for training and 6 for test
Q MAP - PCA
AUC=0.77
Q MAP – MED ROI
Q MAP - LDA
AUC=0.90
18
… some results of map segmentation
FLAIR
5 patients for training and 4 for test
FLAIR - PCA
AUC=0.94
Fluid Attenuated Inversion Recovery
FLAIR – MED ROI
FLAIR - LDA
AUC=0.97
19
Prospects
• Feature reduction or selection?
PCA
LDA
Fisher score
ICA
• Both selection and reduction??
• Fisher score (with a threshold according to
the AUC plateau [as a function of the FS])
• Jaccard Coefficient to set the ‘best’ ANN
threshold for segmentation
• FLAIR segmentation is promising!
20
Changes in glioma water diffusion values after
chemotherapy: work in progress!!
LGG (low-grade glioma) cells grow and diffuse typically along the
white matter tracts
Diffusion Tensor Imaging in glial tumors allows to depict white
matter alterations not visible by conventional MRI
Price et al., Clin Radiol 2003; Wang et al., AJNR 2009
Starting from Diffusion Tensor it is possible to obtain two maps:
isotropic (p) and anisotropic (q)
Pena et al., BJR 2006; Price et al., Eur Radiol 2004
Isotropic (p) and Anisotropic (q) Maps allow a better
characterization of the diffusion features of tumoral and peritumoral
areas
Price et al., AJNR 2006; Price et al., Eur Radiol 2007; Wang et al., AJNR 2009
21
Changes in glioma water diffusion values after
chemotherapy: work in progress!!
Changes in tumor water diffusion occur after successful
treatment and can be attributed to changes in cell density.
p
p
q
q
decrease of tissue infiltration
increase of tissue infiltration
Moffat et al., PNAS 2005
Hamstra et al., JCO 2008
Galban et al., TransOnc 2009
Aim of Study: to investigate whether changes in the Brownian
motion of water within tumor tissue as quantified by using diffusion
MRI could be used in the follow up of treated gliomas.
22
Patients & methods
ID
Age
Site
Previous
surgery
Histology
1p19q
MGMT
Seizures
TMZ (dosedense)
1
34
Frontal L
Oct-05
O II
codel
N/A
No
6 cycles
2
28
Frontal L
Jul-07
OA II
codel
met
No
6 cycles
7
N/A
N/A
9Fronto-tempopatientsJul-04
with low
O II grade glioma
No
insular R
Fronto-tempo-Histology
no
N/A
similar
Sep-07
O II
Yes
insular L
codel
same
duration
ofA treatment
no
Frontal L
Apr-07
II
met
Yes
codel
similar
Clinical HistoryN/A N/A
Fronto-tempoJul-04
A II
Yes
insular L
same scheme of
N/A
N/A
45 neuroradiological
Frontal L
Nov-04
Ofollow-up
II
Yes
8
37
Fronto-tempoinsular L
Sep-09
OA II
no
codel
unmet
Yes
6 cycles
9
32
Frontotemporal R
Mar-08
OA II
no
codel
N/A
No
6 cycles
3
4
5
6
33
25
36

56

6 cycles
6 cycles
6 cycles
6 cycles
6 cycles
23
Patients & methods
IMAGE
ACQUISITION
q and p MAP
IMAGE
COREGISTRATION

3T Scanner Intera Philips Medical
System (gradients 80 mT/m)

MR morphological study: axial T2 TSE
(TR/TE 3000/85, NSA 2), axial FLAIR
(TR/TE/TI 11.000/120/2800) axial FFE
MP-RAGE (TR/TE 8/3.9) voxel size and
positioning as for DTI, acquired following
i.v. injection of paramagnetic contrast

DTI scans: axial Single-Shot Spin Echo
EPI (TR/TE 8986/80, b-value 1000
mm2/sec, 32 directions, SENSE 2.5, FOV
240, 56 sections @ 2.5 mm, repeated
twice)

Diffusion maps: diffusion-tensor
elements calculated and diagonalized at
each voxel, obtaining three eigenvalues,
fractional anisotropy (FA), and trace (Tr)
maps; from the elaboration of these
datasets in MATLAB pure isotropic (p)
and pure anisotropic (q) diffusion maps
are obtained

Segmentation of tumor areas in the
various maps of tensor decomposition
metrics (p, q) obtained from first MR
examination and after five cycles
TUMOR AREA
SEGMENTATION
SEARCH OF
THRESHOLD
PIXEL COLOR MAP
24
Patients & methods
y
y

y>x

p value before chemoterapy
RED
p or q

y<x
x

x
p value before chemoterapy
BLUE
p or q
25
Results
ID
Seizure
response
Radiological
response
(FLAIR)
DTI
DTI response
% blue/red
voxels on
isotropy map
Second surgery
extent of resection
(%)
Peritumoral
IDH1
1
N/A
SD
+
 of isotropy
7.3 blue; 6.1
red
79,72 subtotal
N/A
2
N/A
mR -36.4%
+
 of isotropy
2.7 blue; 2.2
red
83,48 subtotal
N/A
3
N/A
mR -26%
-
 of isotropy
26 blue; 53.5
red
82,67 subtotal
N/A
4
Stable
SD -11.6%
+
 of
isotropy
27 blue;13 red
82,16 subtotal
N/A
5
Stable
PD
-
 of
isotropy
1 blue; 30 red
97,5 subtotal
N/A
6

>50%
SD -9,7%
+
 of isotropy
5.8 blue; 3.7
red
97,7 subtotal
N/A
7

>50%
mR -35%
+
 of isotropy
4.4 blue; 3.1
red
100 total
Neg
8

>50%
SD
+
7 blue; 4.8 red
100 total
Neg
9
N/A
SD
+
67 blue; 17 red
79 subtotal
N/A
 of
isotropy
 of
isotropy
26
M.G., astrocitoma WHO II: fDM su mappa p (isotropia)
dopo 6 cicli TMZ
I esame
II esame
voxel blu: 7%
voxel rossi: 4.8%
stabilità radiologica di malattia
miglioramento clinico
27
R.G., oligodendroglioma WHO II: fDM su mappa p (isotropia)
dopo 6 cicli TMZ
I esame
II esame
voxel blu: 1%
voxel rossi: 30%
progressione radiologica di malattia?
stabilità clinica
progressione di malattia!
28
B.L., oligodendroglioma WHO II: fDM su mappa p (isotropia)
dopo 6 cicli TMZ
I esame
II esame
voxel blu: 7.3%
voxel rossi: 6.1%
stabilità radiologica di malattia
miglioramento clinico
29
B.L., ODG WHO II: confronto con neurofisiologia intraoperatoria
sonda monopolare per la
stimolazione intraoperatoria
 infiltrazione fibre CST
sonda bipolare per la
stimolazione intraoperatoria
infiltrazione fibre CST
30
Some conclusions:
• Tissue analysis with DTI could help the physicians in
evaluating the chemotherapy responses.
• The p or q value variation could suggest a tumor
progression or regression also for cases in which the
tumor volume does not change.
• These preliminary results are in accordance with
neurophysiological results and with intraoperative
bioptic samples.
31
Prospects: we are working to…
• unify in the scatter
plot both the p and the
q value variations
• study also the local
maximum variation
32
Conferences:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
A. CASTELLANO, M. DONATIVI, L. BELLO, G. DE NUNZIO, M. RIVA, G. PASTORE, G. CASACELI, R. RUDÀ, R. SOFFIETTI, AND A.
FALINI(2011). Evaluation of changes in gliomas structural features after chemotherapy using DTI-based Functional Diffusion
Maps (fDMs): a preliminary study with intraoperative correlation. In:2011 Joint Annual Meeting ISMRM-ESMRMB. Montréal,
May 7-13, 2011
G. DE NUNZIO, M. DONATIVI, G. PASTORE, A. CASTELLANO, A. FALINI, L. BELLO, R. SOFFIETTI, (2010). A CAD system for
cerebral glioma and therapy follow-up in Diffusion-Tensor Images. In II Workshop Plasmi Sorgenti Biofisica e Applicazioni,
Lecce (Italy) 26 Ottobre 2010
A. CASTELLANO, L. BELLO, E. FAVA, G. CASACELI, M. RIVA, M. DONATIVI, G. PASTORE, G. DE NUNZIO, R. RUDA', L.
BERTERO, R. SOFFIETTI, A. FALINI (2010) DTI-MR 3D Texture Analysis per la valutazione delle modificazioni delle
caratteristiche strutturali dei gliomi cerebrali dopo trattamento con Temodal: studio preliminare. In XV Congresso Nazionale
della Associazione Italiana di Neuro-Oncologia (AINO), Fiuggi (FR, Italy) 3-6 Ottobre 2010
G. DE NUNZIO, G. PASTORE, M. DONATIVI, A. FALINI, A. CASTELLANO, L . BELLO, R. SOFFIETTI (2010). DT-MR images: A
CAD System for Cerebral Glioma and Therapy Follow-up. In IVth European Conference of Medical Physics - Advances in High
Field Magnetic Resonance Imaging, Udine (Italy) September 22-25 (2010)
G. DE NUNZIO, M. DONATIVI, G. PASTORE, A. CASTELLANO, G. SCOTTI, L. BELLO, A. FALINI (2010). Automatic
Segmentation and Therapy Follow-up of Cerebral Glioma in Diffusion-Tensor Images. In 2010 IEEE International Conference
on Computational Intelligence for Measurement Systems and Applications (CIMSA 2010). Taranto (Italy) September 6-8, 2010
CASTELLANO, L. BELLO, E. FAVA, M. RIVA, G. CASACELI, G. DE NUNZIO, M. DONATIVI, G. PASTORE, R. RUDA', R. SOFFIETTI,
A. FALINI (2010) Changes in gliomas structural features after Temodal treatment evaluated by DTI-MR texture analysis: a
preliminary study. In 9th International Meeting UPDATES IN NEURO-ONCOLOGY, Brain Tumor Symposium, Cortona (AR,
Italy), July 2-4, 2010
G. DE NUNZIO, G. PASTORE, M. DONATIVI, A. CASTELLANO, A. FALINI (2010). A CAD system for cerebral glioma based on
texture features in DT-MR images. In International Conference on Imaging Techniques in Subatomic Physics, Astrophysics,
Medicine and Biology (Imaging 2010), Stockholm (Sweden) 8-11 June 2010
G. DE NUNZIO, A. CASTELLANO, G. PASTORE, M. DONATIVI, G. SCOTTI, L. BELLO, A. FALINI (2010). Semi-automated
evaluation of structural characteristics and extension of cerebral gliomas using DTI-MR 3D Texture Analysis. In: 2010 Joint
Annual Meeting ISMRM-ESMRMB. Stockholm, May 1-7, 2010
G. DE NUNZIO, A. CASTELLANO, M. DONATIVI, G. PASTORE, A. FALINI. (2010). A semi-automated DTI-based approach to
evaluate structural characteristics and extension of cerebral gliomas (poster No C-2926). In: European Congress of Radiology
(ECR2010). Vienna, March 4-8, 2010
G. DE NUNZIO, G. PASTORE, A. CASTELLANO, M. DONATIVI, A. FALINI (2010). Automatic Segmentation of Cerebral Glioma in
DT-MR Images by 3D Texture Analysis. In: Risonanza magnetica in medicina: dalla ricerca tecnologica avanzata alla pratica
clinica (Italian Chapter of the International Society of Magnetic Resonance in Medicine). Milano, 4-5 febbraio 2010
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34
Metodi per la riduzione dello spazio delle feature ai fini della
classificazione
 ICAdecompone il dataset nelle sue
sottoparti indipendenti

Dato il vettore x, mistura dei segnali originali s tramite una
matrice di mixing A

scopo della ICA è identificare una matrice di de-mixing W
tale che le componenti del vettore in uscita siano quanto più
statisticamente indipendenti
35
ICA
Per stimare una delle IC
La
combinazione
lineare
delle sorgenti indipendenti è
più
“gaussiana”
delle
componenti originarie e lo
diventa “al minimo” quando z
ha solo l’i-imo elemento non
nullo: questo porta a scegliere
W in modo da massimizzare la
non-gaussianità di WTx
36
Features
37
Linear Discriminant Analysis
Within-class scatter matrix
Between-class scatter matrix
Sw 
c
ni
 (Y
j
 M i )(Y j  M i )T
i 1 j 1
Sb 
c
 (M
i
 M )( M i  M )T
i 1
projection matrix
y U x
T
− LDA computes a transformation that maximizes the between-class
scatter while minimizing the within-class scatter:
| Sb |
| U T SbU |
max
 max T
| U S wU |
| Sw |
products of eigenvalues !
S w1Sb  U U T
Sb , S w : scatter matrices of the y data after projection
38
Linear Discriminant Analysis
• Does Sw-1 always exist?
− If Sw is non-singular, we can obtain a conventional eigenvalue
problem by writing:
S w1Sb  U U T
− In practice, Sw is often singular since the data are image vectors
with large dimensionality while the size of the data set is much
smaller (M << N )
− Since Sb has at most rank C-1, the max number of eigenvectors
with non-zero eigenvalues is C-1 (i.e., max dimensionality of sub39
space is C-1)
Features in MaZda VS Features in Matlab:
an example
40