M. Hatt (presented by Simon David)

From nebulae segmentation in

astronomical imaging to tumor

delineation in 18F-FDG PET imaging:

how can one serve the other?

M. Hatt1, C. Collet2, F. Salzenstein3, C. Roux1, D. Visvikis1

Speaker: S. David1

1. LaTIM, INSERM U650, Brest, France1. LaTIM, INSERM U650, Brest, France

2. LSIIT, CNRS - UMR 7005, Strasbourg, France2. LSIIT, CNRS - UMR 7005, Strasbourg, France

3. INESS, CNRS - UMR 7163, Strasbourg, France3. INESS, CNRS - UMR 7163, Strasbourg, France

Context and objectiveCancer Oncology

Gold standard for diagnosis

Other applications of interest:

Radiotherapy planning

Prognosis, therapy assessment

PET/CT multimodality imaging

Quantification active biological volume uptake measurement radiotherapy target definition

Requires the definition of a volume of interest

Computed tomography (CT)

Positron Emission Tomography (PET)

Source of image X-ray Positron emitter (18F)

Nature Anatomic: tissues and bones density

Functional : accumulation of radioactive tracer

Resolution < 1 mm > 5 mm

Imaging for oncology

Context and objective Problems of PET images

3

Noise

(acquisition variability)

Blur

(spatial resolution)

Voxels size

(grid spatial sampling)

uptake heterogeneities within the tumor

Methodologies Existing solutions

Manual definition of regions of interest in the background

Parameters optimization for each scanner

Assume tumors are homogeneous spheres :

Threshold-based methodologies [1-3]

[1] J. A. van Dalen et al, Nuclear Medicine Communications, 2007

[2] U. Nestle et al, Journal of Nuclear Medicine, 2005

[3] J.F. Daisne et al, Radiotherapy Oncology, 2003

Require a lot of a priori information and are system and user dependent

But tumors are often of complex shapes and heterogeneous !

PET images share several characteristics with some astronomical images

Why looking at astronomical images processing for solutions ?

The segmentation/classification field is more mature for astronomy than PET

Methodologies Astronomical images segmentation

Nebulae vs PET tumor ?


Nebulae vs PET tumor ?

Characteristic Nebulae image PET tumor image

Dimensions 2D, multi/hyper spectral 3D, mono spectral

Definition Large (~512x512) Small (~30x30x30)

Encoding 32b real 16b/32b real

Fuzzy yes yes

Noisy yes yes

Band 1 Band 2 Band 3

Slice n+1

Slice n

Slice n-1

Use of statistical image processing to deal with the noise, combined with fuzzy modeling to deal with blur


Methodology : statistical + fuzzy

Probabilistic / statistical part models the uncertainty of classification

Fuzzy part models the imprecision of acquired data

Combining both to model astronomical or PET images characteristics

1 2 ... C

c : Discrete Dirac measure on class c

Standard (“hard”) statistical modelling

Ground-truth

0 1

: Continuous Lesbegue measure on 0,1

Fuzzy modelling [1] [2]

[1] H. Caillol et al, IEEE Transactions on Geoscience Remote Sensing, 1993

[2] F. Salzenstein and W. Pieczynski, CVGIP : Graphical Models and Image Processing, 1997

Methodologies

Methodology: fuzzy Markov chains

Markov assumption:

1 1 1( | ,..., ) ( | )t t t tp x x x p x x

… …1x 2x tx Tx

1( | )t tp x x

Transition probabilities

1( )p xInitial

probabilities

Use of the Hilbert-Peano path to transform 2D image into 1D chain

1tx

1y 2y ty TyObservation

vector( | )t tp y x1ty

in [0,1]tx

Methodologies

Result on Nebulae

Fuzzy Hidden Markov Chains (FHMC) multispectral segmentation

F. Salzenstein, C. Collet, S. Lecam, M. Hatt, Pattern Recognition Letters, 2007

Methodologies

Apply to PET ?

3D PET tumor

Iterative stochastic estimation (SEM)

1D chain with discrete values {0,1,F1,F2}

Segmentation (MPM)

1D chain with real values

Hilbert-Peano 3D

Inverse Hilbert-Peano 3D

Segmentation map (2 fuzzy levels)

Extended Hilbert-Peano path to transform 3D image into 1D

M. Hatt et al, Physics in Medicine and Biology, 2007;52(12):3467-3491

Methodologies

Problem !

3D Hilbert-Peano path to transform 3D image into 1D disrupts spatial correlation :

Neighbors voxels in the image may be far from each other in the chain

Size of tumors with respect to object and size of voxels leads to large errors for small tumors !

M. Hatt et al, Physics in Medicine and Biology, 2007; 52(12):3467-3491

Methodologies

Solution: locally adaptive method

3D PET tumor

Segmentation map

Segmentation map

FHMC

M. Hatt et al, IEEE Transactions on Medical Imaging, 2009;28(6):881-893

Iterative stochastic estimation (SEM)

Segmentation

Markovian model replaced by sliding estimation cube to compute probabilities for each voxel regarding its neighbors :

FLAB (Fuzzy Locally Adaptive Bayesian) method

Methodologies

FLAB

1

2

3

M. Hatt et al, International Journal of Radiation Oncology Biology Physics , in press, 2009

Modeling fuzzy transitions between pairs of hard classes

to deal with heterogeneities

2 hard classes and 1 fuzzy transition

1

0

Methodologies

3 hard classes and 3 different fuzzy

transitions

Simple phantom validationResults

Phantom acquisitions with spheres : 37 to 10 mm in diameter

Phantom Computed tomography image (truth)

18F Positron Emission Tomography image

Axial Coronal Sagital

Results FHMC vs FLAB

M. Hatt et al, IEEE Transactions on Medical Imaging, 2009;28(6):881-893

Multiple scanners robustness validation

4 different scanner models and various acquisitions parameters (contrast, noise, reconstruction algorithms, size of voxels…)

Philips Gemini GE Discovery LSOSEM

Siemens BiographRAMLA 3D

Philips Gemini TFTF MLEM

A

B

1 2 1 1 21 2

A = 4:1 or 5:1, B = 8:1 or 10:1 1 = 2x2 mm, 2 = 4x4 or 5x5 mm

37 mm28 mm22 mm17 mm13 mm

M. Hatt et al, Society of Nuclear Medicine annual meeting, Toronto, Canada, 2009

Results

Real Simulated

Small homogeneous Large heterogeneous

Real Simulated

20 tumors (NSCLC, H&N, Liver) maximum diameter from 12 to 82 mm Heterogeneities: from none to high Shapes: from almost spherical to complex Simulated with Monte Carlo GATE (Geant4 Application for Tomography Emission)


Results Accuracy validation on simulated data

FLAB

Ground-truth

Fixed threshold

Classif. error: 6%> 100%Simulated PET

Adaptive threshold

Classification errors

Grey region 4%

Black region 2%

Volume error

-62%

Volume error

+37%

Segmentation

Segmentation

Adaptive threshold

FLAB

Fixed threshold

Ground-truth

Simulated PET

14%


Results Accuracy validation on simulated data

Patients with histology accuracy validation

18 tumors (NSCLC) with histology study [1]

maximum diameter from 15 to 90 mm (mean 44, SD 21) Heterogeneity : none to high Shapes : from almost spherical to complex

CT

PET

[1] A. van Baardwijk et al, International Journal of Radiation Oncology Biology Physics, 2007

Results

Patient with NSCLC

FLABAdaptive thresholdFixed threshold (42%)


Results Patients with histology accuracy validation

Conclusions and work in progress

Studies are ongoing to further investigate the clinical impact of the proposed methodology in radiotherapy or patient prognosis and therapy assessment

This work is a good example of know-how transfer from astronomical to medical imaging

Once adapted to PET data (2D->3D, spatial modeling), statistical and fuzzy segmentation developed for astronomical imaging performed admirably well for tumor delineation

Thank you for your attention

M. Hatt (presented by Simon David)

Technology

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