Giorgio De Nunzio 1, Marina Donativi 1, Gabriella Pastore 2, Matteo Rucco 3, Antonella Castellano 4,...

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)

MAGIC5 meeting - Genova 2011/5/4-6

A CAD system for cerebral glioma and therapy follow-up in DTI and FLAIR: status report

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GliomaGlioma

•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 ofwater molecules

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Materials and methodsMaterials and methods

Glioma in MR-DTI

CAD System for glioma

segmentation (texture analysis)

Glioma structure during follow-up

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svoicsvoi

rsvoi

FEATURE SELECTION

Training stepTraining step

ANN(learn)

IMAGE ACQUISITION

ROI CREATION

SLIDING WINDOW

MAZDA

SCATTER PLOT

PCA

IMAGE

ACQUISITION GUI

SLIDING WINDOW MAZDA

PCAANN(classification)

CADCAD

ROICREATION

Once upon a time…Once upon a time…

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svoicsvoi

rsvoi

FEATURESELECTION

Training stepTraining step

ANN(learn)

IMAGE ACQUISITION

ROI CREATION

SLIDING WINDOW

69 FEATURES

PCALDA

IMAGE

ACQUISITION SLIDING WINDOW 69 FEATURE

PCALDA

ANN(classification)

CADCAD

ROICREATION

ALL IN MATLAB

APPLY FILTER

FEATURESELECTION

NOW!!!NOW!!!

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Fisher’s score vs featureFisher’s score vs feature

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PCA or LDA?

Work in progress…Work in progress…

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Why Principal Component Analysis?Why Principal Component Analysis?

Maximize variance by axis transformation

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0

5

10

15

20

25

PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10

Vari

an

ce (

%)

Dimensionality ReductionDimensionality Reduction Can ignore the components of lower significance.

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

Vari

ance

Dimensionality

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Limitations of PCALimitations of PCA

Are the maximum-variance variables the relevant features for discrimination preservation?

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Linear Discriminant AnalysisLinear 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.

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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:

and obtain the eigenvalues of the linear transformation Matrix T, that is

Starting from that, it is possible to calculate the PC’s

Feature dimensionality reduction methods

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

0

5

10

15

20

25

PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10

Vari

an

ce (

%)

Within-class scatter matrix

Between-class scatter matrix

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PCA LDA

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PCALDA

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-1.55 -1.5 -1.45 -1.40

50

100

150

200

250

300

350

400

450LDA

… … Classifier:Artificial Neural Networks (ANN)Classifier:Artificial Neural Networks (ANN)

AUC=0.94AUC=0.97

FLAIR

5 patients for training and 4 for test

Back-propagation feed-forward ANN:

• 1 hidden layer, with 3 neurons

• 1 output neuron

PCA LDA

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… … some results of map creation and segmentationsome 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)

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P MAP - PCA P MAP - LDA

… … some results of map segmentationsome results of map segmentation

P MAP


AUC=0.88 AUC=0.95

P MAP – MED ROI

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Q MAP - PCA Q MAP - LDAQ MAP – MED ROI


Q MAP


AUC=0.77 AUC=0.90

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FLAIR - PCA FLAIR - LDAFLAIR – MED ROI


FLAIR


AUC=0.94 AUC=0.97

Fluid Attenuated Inversion Recovery

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ProspectsProspects

• Feature reduction or selection?

PCA Fisher score LDA 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!FLAIR segmentation is promising!

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Changes in glioma water diffusion values after Changes in glioma water diffusion values after chemotherapy: work in progress!!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)

Isotropic (p) and Anisotropic (q) Maps allow a better characterization of the diffusion features of tumoral and peritumoral areas

Pena et al., BJR 2006; Price et al., Eur Radiol 2004

Price et al., AJNR 2006; Price et al., Eur Radiol 2007; Wang et al., AJNR 2009

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Changes in tumor water diffusion occur after successful treatment and can be attributed to changes in cell density.

increase of tissue infiltration

decrease of tissue infiltration

p q

p q

Moffat et al., PNAS 2005Hamstra 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.

Changes in glioma water diffusion values after Changes in glioma water diffusion values after chemotherapy: work in progress!!chemotherapy: work in progress!!

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ID Age SitePrevious surgery

Histology1p-19q

MGMT SeizuresTMZ (dose-dense)

1 34Frontal L Oct-05 O II codel N/ANo 6 cycles

2 28Frontal L Jul-07 OA II codel metNo 6 cycles

3 33Fronto-tempo-insular R

Jul-04 O IIN/A N/A

No 6 cycles

4 25Fronto-tempo-insular L

Sep-07 O IIno

codelN/A

Yes 6 cycles

5 36Frontal L Apr-07 A IIno

codelmetYes 6 cycles


Jul-04 A IIN/A N/A

Yes 6 cycles

7 45Frontal L Nov-04 O IIN/A N/A

Yes 6 cycles


Sep-09 OA IIno

codelunmetYes 6 cycles

9 32Fronto-temporal R

Mar-08 OA IIno

codelN/ANo 6 cycles

9 patients with low grade glioma similar Histology same duration of treatment similar Clinical History same scheme of

neuroradiological follow-up

Patients & methodsPatients & methods

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Patients & methodsPatients & methods 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

IMAGE ACQUISITION

IMAGE COREGISTRATION

SEARCH OF THRESHOLD

PIXEL COLOR MAP

q and p MAP

TUMOR AREA SEGMENTATION

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y>x

p value before chemoterapy x

y

p value before chemoterapy x

y

y<x

p or q p or q

BLUERED

Patients & methods

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IDSeizure response

Radiological response (FLAIR)

DTI DTI response% blue/red voxels on

isotropy map

Second surgeryextent of resection

(%)

Peritumoral IDH1

1 N/A SD + of isotropy7.3 blue; 6.1 red

79,72 subtotal N/A

2 N/A mR -36.4% + of isotropy2.7 blue; 2.2 red

83,48 subtotal N/A

3 N/A mR -26% - of isotropy26 blue; 53.5 red

82,67 subtotal N/A

4 Stable SD -11.6% + of

isotropy27 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 isotropy5.8 blue; 3.7 red

97,7 subtotal N/A

7 >50%

mR -35% + of isotropy4.4 blue; 3.1 red

100 total Neg

8 >50%

SD + of isotropy 7 blue; 4.8 red 100 total Neg

9 N/A SD + of isotropy 67 blue; 17 red 79 subtotal N/A

ResultsResults

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voxel blu: 7%voxel blu: 7%voxel rossi: voxel rossi: 4.84.8%

M.G., astrocitoma WHO II: fDM su mappa p (isotropia) M.G., astrocitoma WHO II: fDM su mappa p (isotropia) dopo 6 cicli TMZdopo 6 cicli TMZ

I esame I esame II esame II esame

stabilità radiologicastabilità radiologica didi malattiamalattiamiglioramento clinicomiglioramento clinico

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voxel blu: 1%voxel blu: 1%voxel rossi: 30voxel rossi: 30%

R.G., oligodendroglioma WHO II: fDM su mappa p R.G., oligodendroglioma WHO II: fDM su mappa p (isotropia) (isotropia) dopo 6 cicli TMZdopo 6 cicli TMZ

progressione diprogressione di malattia!malattia!


progressione radiologicaprogressione radiologica didi malattia?malattia?

stabilità clinicastabilità clinica

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voxel blu: 7.3%voxel blu: 7.3%voxel rossi: voxel rossi: 6.16.1%


B.L., oligodendroglioma WHO II: fDM su mappa p B.L., oligodendroglioma WHO II: fDM su mappa p (isotropia) (isotropia) dopo 6 cicli TMZdopo 6 cicli TMZ

stabilità radiologicastabilità radiologica didi malattiamalattiamiglioramento clinicomiglioramento clinico

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sonda monopolare per la sonda monopolare per la stimolazione intraoperatoriastimolazione intraoperatoria

infiltrazione fibre CST infiltrazione fibre CST

B.L., ODG WHO II: confronto con neurofisiologia B.L., ODG WHO II: confronto con neurofisiologia intraoperatoriaintraoperatoria

infiltrazione fibre CST infiltrazione fibre CST

sonda bipolare per la sonda bipolare per la stimolazione intraoperatoriastimolazione intraoperatoria

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• Tissue analysis with DTI could help the physicians in Tissue analysis with DTI could help the physicians in evaluating the chemotherapy responses.evaluating the chemotherapy responses.

• The p or q value variation could suggest a tumorThe p or q value variation could suggest a tumor progression or regression also for cases in which theprogression or regression also for cases in which the tumor volume does not change.tumor volume does not change.

• These preliminary results are in accordance with These preliminary results are in accordance with neurophysiological results and with intraoperativeneurophysiological results and with intraoperative bioptic samples.bioptic samples.

Some conclusionsSome conclusions:

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Prospects: we are working to…Prospects: we are working to…

• unify in the scatter plot both the p and the q value variations

• study also the local maximum variation

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Conferences:Conferences:1. 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

2. 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

3. 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

4. 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)

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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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Metodi per la riduzione dello spazio delle feature ai fini della classificazione

ICAdecompone 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

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ICAPer 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

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Features

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1 1

1

( )( )

( )( )

incT

w j i j ii j

cT

b i ii

S Y M Y M

S M M M M

Within-class scatter matrix

Between-class scatter matrix

TUy xprojection matrix

− LDA computes a transformation that maximizes the between-class scatter while minimizing the within-class scatter:

| | | |max max

| || |

Tb b

Tww

S U S U

U S US

products of eigenvalues !

,b wS S : scatter matrices of the y data after projection

1w

TbS S U U

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− 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 sub-space is C-1)

• Does Sw-1 always exist?

− If Sw is non-singular, we can obtain a conventional eigenvalue problem by writing:

− 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 )

1w

TbS S U U

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Features in MaZda VS Features in Matlab: Features in MaZda VS Features in Matlab: an examplean example

Giorgio De Nunzio 1, Marina Donativi 1, Gabriella Pastore 2, Matteo Rucco 3, Antonella Castellano 4,...

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