From nebulae segmentation in astronomical imaging to tumor delineation in 18F-FDG PET imaging: how...
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Transcript of From nebulae segmentation in astronomical imaging to tumor delineation in 18F-FDG PET imaging: how...
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 ?
Methodologies Astronomical images segmentation
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
Methodologies Astronomical images segmentation
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)
M. Hatt et al, International Journal of Radiation Oncology Biology Physics , in press, 2009
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%
M. Hatt et al, International Journal of Radiation Oncology Biology Physics , in press, 2009
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%)
M. Hatt et al, International Journal of Radiation Oncology Biology Physics , in press, 2009
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