Transcript 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, France
2. LSIIT, CNRS - UMR 7005, Strasbourg, France
3. INESS, CNRS - UMR 7163, Strasbourg, France
Context and objective
Imaging for oncology
Cancer
Oncology
PET/CT multimodality imaging

Gold standard for diagnosis

Other applications of interest:
 Radiotherapy planning
 Prognosis, therapy assessment
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
< 1 mm
> 5 mm
Resolution
Quantification
 active biological volume
 uptake measurement
 radiotherapy target definition
Requires the definition of a
volume of interest
Context and objective
Problems of PET images
 Noise
(acquisition variability)
 Blur
(spatial resolution)
 Voxels size
(grid spatial sampling)
 uptake heterogeneities
within the tumor
3
Methodologies
Existing solutions
Threshold-based methodologies [1-3]
Require a lot of a priori information and are system and user dependent
 Manual definition of regions of interest in the background
 Parameters optimization for each scanner
 Assume tumors are homogeneous spheres :
But tumors are often of complex shapes and heterogeneous !
[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
Methodologies
Astronomical images segmentation
Why looking at astronomical images processing for solutions ?
 PET images share several characteristics with some astronomical images
 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 ?
Slice n+1
Slice n
Band 1
Band 2
Slice n-1
Band 3
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
Use of statistical image processing to deal with the noise, combined with fuzzy
modeling to deal with blur
Methodologies
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
Ground-truth
Standard (“hard”) statistical modelling
  1   2  ...   C
c

Fuzzy modelling [1] [2]
   0  1  
: Discrete Dirac measure on class c
: Continuous Lesbegue measure on 0,1
[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:
p( xt | x1 ,..., xt 1 )  p( xt | xt 1 )
Initial probabilities
p( x1 )
x1
x2
…
Transition probabilities
p( xt | xt 1 )
xt 1
xt
in [0,1]
…
xt
xT
Observation vector
y1
y2
yt 1
yt
 Use of the Hilbert-Peano path to transform 2D image into 1D chain
p( yt | xt )
yT
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 ?
 Extended Hilbert-Peano path to transform 3D image into 1D
1D chain with real values
Hilbert-Peano 3D
3D PET
tumor
Iterative stochastic estimation (SEM)
Segmentation (MPM)
Segmentation map
(2 fuzzy levels)
Inverse HilbertPeano 3D
1D chain with discrete values {0,1,F1,F2}
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
 Markovian model replaced by sliding estimation cube to
compute probabilities for each voxel regarding its neighbors :
 FLAB (Fuzzy Locally Adaptive Bayesian) method
3D PET
tumor
Iterative stochastic estimation (SEM)
Segmentation
Segmentation
map
Segmentation
map
FHMC
FLAB
M. Hatt et al, IEEE Transactions on Medical Imaging, 2009;28(6):881-893
Methodologies
to deal with heterogeneities
 Modeling fuzzy transitions between pairs of hard classes
2 hard classes and
1 fuzzy transition
3 hard classes and 3
different fuzzy transitions
0
1
1
3
2
M. Hatt et al, International Journal of Radiation Oncology Biology Physics , in press, 2009
Results
Simple phantom validation
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
Results
Multiple scanners robustness validation
Volume Error (%)
42% threshold
FLAB
 4 different scanner models and various acquisitions parameters
100
(contrast, noise, reconstruction algorithms, size of voxels…)
A
B
90
80
70
RAMLA 3D
60 Philips Gemini
50
40
30
20
10
0
-10
-201
2
13 mm
TF MLEM
Philips Gemini TF
1
17 mm
2
22 mm
GE Discovery LS
1
OSEM
28 mm
Siemens Biograph
1
A = 4:1 or 5:1, B = 8:1 or 10:1
1 = 2x2 mm, 2 = 4x4 or 5x5 mm
Sphere diameter
M. Hatt et al, Society of Nuclear Medicine annual meeting, Toronto, Canada, 2009
2
37 mm
Results
Accuracy validation on simulated data
 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)
Small homogeneous
Real
Large heterogeneous
Simulated
Real
Simulated
M. Hatt et al, International Journal of Radiation Oncology Biology Physics , in press, 2009
Results
Accuracy validation on simulated data
Fixed threshold
Segmentation
70
Classification Error (%)
Ground-truth
60 Simulated
PET
50
40
Ground-truth
Classif. error:
Adaptive threshold
Mean error and associated std. dev.
14%
> 100%
Simulated
PET
30
6%
Volume
error
Volume
error
-62%
+37%
Fixed threshold
20
FLAB
10
Adaptive threshold
Classification errors
Grey region 4%
0
FLAB
Segmentation
Adaptive threshold
42% threshold
Black region
2%
Segmentation algorithms
FLAB
M. Hatt et al, International Journal of Radiation Oncology Biology Physics , in press, 2009
Results
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
Patients with histology accuracy validation
M. Hatt et al, International Journal of Radiation Oncology Biology Physics , in press, 2009
Patient with NSCLC
25
20
Error (%) with respect to
true maximum diameter
15
10
5
0
-5
-10
-15
-20
FLAB
Adaptive threshold
Fixed thresholdSegmentation
(42%) algorithms
Adaptive threshold
Fixed threshold
FLAB
Conclusions and work in progress
 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
 Studies are ongoing to further investigate the clinical impact
of the proposed methodology in radiotherapy or patient
prognosis and therapy assessment
Thank you for your attention