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IntroductionMethodology
Experimental evaluationConclusions
3D Riesz–wavelet Based Covariance Descriptorsfor Texture Classification ofLung Nodule Tissue in CT
Pol Cirujeda./, Henning Muller†, Daniel Rubin?,Todd A. Aguilera?, Billy W. Loo Jr.?,
Maximilian Diehn?, Xavier Binefa./, Adrien Depeursinge†,‡
./ † ? ‡
SaAT6.6
August 29th, 20151 / 18
IntroductionMethodology
Experimental evaluationConclusions
Motivation / ContributionRelated Work
Motivation + Contribution
I (Statistical) feature extraction and representation
I Dictionary modelling of lung/nodule tissue areas
I Classification of lung nodule areas(solid / ground glass opacity -GGO / healthy)
Goal and application
Supervised learning of region classes from textureRobustness to size and shape variations
Applications: tissue modelling, classification, segmentation
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IntroductionMethodology
Experimental evaluationConclusions
Motivation / ContributionRelated Work
Motivation + Contribution
I (Statistical) feature extraction and representation
I Dictionary modelling of lung/nodule tissue areas
I Classification of lung nodule areas(solid / ground glass opacity -GGO / healthy)
Goal and application
Supervised learning of region classes from textureRobustness to size and shape variations
Applications: tissue modelling, classification, segmentation
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IntroductionMethodology
Experimental evaluationConclusions
Motivation / ContributionRelated Work
Related work
Clinical domain
I 3D visualization and annotation software
I Manual delineation with expertise of clinicians
Computer vision + Machine Learning domain
I 3D features: Riesz transform, 1st and 2nd order visual cues
I 3D descriptors: MCOV, 3D-SIFT, SHOT, THRIFT...
I Linear/non-linear (un)supervised classification methods:CNN, Kernel-SVMs, Sparse coding, Bag-of-visual features...
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IntroductionMethodology
Experimental evaluationConclusions
Motivation / ContributionRelated Work
Related work
Clinical domain
I 3D visualization and annotation software
I Manual delineation with expertise of clinicians
Computer vision + Machine Learning domain
I 3D features: Riesz transform, 1st and 2nd order visual cues
I 3D descriptors: MCOV, 3D-SIFT, SHOT, THRIFT...
I Linear/non-linear (un)supervised classification methods:CNN, Kernel-SVMs, Sparse coding, Bag-of-visual features...
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IntroductionMethodology
Experimental evaluationConclusions
IntuitionRiesz-Covariance descriptorsData and patientsClassification
Intuition - features
Riesz-wavelet transform as texture features 1
Figure 1: Lung nodule CT slice with corresponding Riesz filter responses
1A. Depeursinge et al., ”Lung Texture Classification Using Locally–Oriented RieszComponents”, in MICCAI 2011
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IntroductionMethodology
Experimental evaluationConclusions
IntuitionRiesz-Covariance descriptorsData and patientsClassification
Intuition - feature representation (in 3D)
3D Riesz-covariance models
Figure 2: 3D CT volume and associated Riesz-covariance descriptor
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IntroductionMethodology
Experimental evaluationConclusions
IntuitionRiesz-Covariance descriptorsData and patientsClassification
3D Riesz-Covariance descriptors
x y
zv
Φ(ct, v) = {φx ,y ,z , ∀x , y , z ∈ v} (1)
φx ,y ,z =(R(n1,n2,n3)
x ,y ,z , ‖R‖x ,y ,z , ctx ,y ,z)
(2)
RieszCov (Φ(ct, v)) =1
N − 1
N∑i=1
(φx ,y ,z − µφ)) (φx ,y ,z − µφ))T ,
(3)
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IntroductionMethodology
Experimental evaluationConclusions
IntuitionRiesz-Covariance descriptorsData and patientsClassification
Covariance model benefits
I Common framework for statistical data modelling.
I Features ≡ samples of n−dim joint distributions.
I Second order moment statistics (n × n covariance matrices).
I Covariances manifold (Sym+d ) ⇒ analytical modelling
Riemannian space ported machine learning techniques.
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IntroductionMethodology
Experimental evaluationConclusions
IntuitionRiesz-Covariance descriptorsData and patientsClassification
Covariance descriptors - Sym+d Riemannian space
logId
TId
x = logY (X ) = Y12 log
(Y−
12XY−
12
)Y
12 (4)
x = vect(x) = (x1,1, x1,2, ..., x1,d , x2,2, x2,3, ..., xd ,d) (5)
δ(X ,Y ) =
√Trace
(log(X−
12YX−
12
))(6)
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IntroductionMethodology
Experimental evaluationConclusions
IntuitionRiesz-Covariance descriptorsData and patientsClassification
Data gathering
Ground-truth:
I 95 patients (from Stanford Hospital and Clinics)
I Biopsy-proven early stage non-small cell lung carcinoma
I Nodule regions delineated in CTs by clinicians
I Processing with MATLAB software:isotropic voxels of 0.8 mm3
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IntroductionMethodology
Experimental evaluationConclusions
IntuitionRiesz-Covariance descriptorsData and patientsClassification
Data samples
GGO vs. Solid lung nodule tissue components (vs. healthy lung)
Figure 3: Lung nodule Riesz filterresponses - GGO component
Figure 4: Lung nodule Riesz filterresponses - solid component
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IntroductionMethodology
Experimental evaluationConclusions
IntuitionRiesz-Covariance descriptorsData and patientsClassification
Bag-of-covariances
Standard bag-of-visual features paradigm.Sub-sampling of partial class regions for a complete dictionarymodelling
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IntroductionMethodology
Experimental evaluationConclusions
IntuitionRiesz-Covariance descriptorsData and patientsClassification
Bag-of-covariances
I Dictionary D ≡ x cv ,p = vect(logId (RieszCovCV ,P))
I Training set: modelling frequencies of words in D
I Classification decision: class(ct) = argmini D(hct , hi )
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IntroductionMethodology
Experimental evaluationConclusions
Experimental setup
Experimental setup
Data sets:
I 35 patients for model learning, 60 for test set.
I 60 words per class (dictionary size, 3× 60× 35 = 3600)
I 10-fold cross-validation.
Quantitative evaluation w.r.t. ground-truth:
I Avg. sensitivity (TP / TP+FN) = 82.2%
I Avg. specificity (TN / TN+FP) = 86.2%
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IntroductionMethodology
Experimental evaluationConclusions
RemarksFuture work
Conclusions
I Computer vision and Machine Learning to the service ofmedical knowledge
I Statistical design for a robust solution
I Easily extendible framework
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IntroductionMethodology
Experimental evaluationConclusions
RemarksFuture work
Future work
I More patients and bigger data collection
I Different modelling contexts for concrete problems
I Exploit the covariance-based descriptor space for differentMachine Learning techniques (clustering, classification,regression...)
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IntroductionMethodology
Experimental evaluationConclusions
RemarksFuture work
Thanks for your attention
Questions?
3D Riesz–wavelet Based Covariance Descriptorsfor Texture Classification ofLung Nodule Tissue in CT
SaAT6.6
Pol Cirujeda, UPF
EMBC 2015, August 29th 2015
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