Download BioMedical Informatics Lab Digital Patient ERCIM WG Meeting, Nice

Multi-level biomedical data
analysis and modelling
K. Marias
Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Summary
The talk will summarise the work carried
out at FORTH, concerning multi-level
biomedical information analysis & network
analysis and visualization, and modelling.
Discuss the ‘blending’ of models of
pathophysiology with actual biomedical
measurements in order to further inspire
but also to validate them.
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Purpose of this talk
Multi-level (from molecular to tissue/organ)
data analysis problems related to
modelling
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems
Reality in practice…
In order to be ‘individualized’, In Silico Models should be
linked to multi-level biomedical data
Multi-level data
MODEL
C
 J  k ( x)C
t
C
 Dc  2C  k ( x)C
t
Information v  kp
extraction n
 (vn )  P
t
 (kp)   P

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t Meeting, Nice
Digital Patient ERCIM WG
BioMedical Informatics Lab
Multi-level data analysis problems
Multi-level Models and Data
However, it isn’t straight forward how measurements (e.g.
pixel values) can be translated to physiological parameters,
which are essential to compute for modelling human
processes
Information extraction isn’t straight forward since there are
differences (but also complementarity!) in all scales…
Multi-level data
TEMPORAL ANALYSIS IS IMPORTANT IN ALL
SCALES!!!
MODEL
Information
extraction
C
 J  k ( x)C
t
C
 Dc 2C  k ( x)C
t
v  kp
n
 (vn )  P
t
 (kp)   P

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t
Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems
What is needed for multi-level
physiological information extraction
at all scales
•
•
•
•
•
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Geometrical normalisation
Extraction of relevant information
Intensity normalization
Quantification
Visualisation
Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Geometry Correction
Temporal Biomedical Data
Measurements
Temporal Biomedical Data
measurements of properties,
physiological processes, etc.
(e.g. X-ray attenuation,
radiopharmaceutical
distribution, geneExpression, etc)
Consistent Geometry
and
Values reflecting
Physiological
Parameters
Sources of Variability:
Patient positioning/motion
Sample Preparation
Scanner Parameters
Variable representation of
the same object, etc.
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Geometry Correction
True Assessment of temporal change
Kostas Marias, Christian Behrenbruch, Santilal Parbhoo, Alexander Seifalian and Sir Michael Brady, “A Registration Framework for the Comparison of
Mammogram Sequences”, IEEE Transactions on Medical Imaging (June, 2005).
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Geometry Correction
Therapy Evaluation
99mTc Sestamibi scintimammograms showing the effect of chemotherapy.
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Geometry Correction
Quantification Example, 46 year old patient,
infiltrating, grade 3, node positive
Partial response (31% reduction) following a 10 week course
of chemotherapy
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Geometry Correction
Molecular imaging
Current work and aims:
Cell level:
cell-trafficking studies of tumour metastases, immune cell
response and transplantation of stem cells and various
precursor cells.
Gene level:
developing gene-therapies, visualization of regulation of
gene-expression
and
intracellular
protein-protein
interactions.
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Geometry Correction
Correcting time dependent geometries in 2D
molecular optical imaging studies
Simulated experiments
six marker registration assessment in an anesthetized mouse
Kostas Marias, Jorge Ripoll, Heiko Meyer, Vasilis Ntziachristos, Stelios Orphanoudakis, “Image Analysis for Assessing Molecular Activity Changes in TimeDependent Geometries”, IEEE Transactions on Medical Imaging, Special issue on Molecular Imaging (July, 2005).
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Geometry Correction
Correcting time dependent geometries in 2D
molecular optical imaging studies
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Geometry Correction
Microarrays-Registration
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Information Extraction
• Essentially, this implies the use of some kind of
process (e.g. segmentation) to identify important
structures and features in the images
• Tumours can be segmented using a
pharmacokinetic model of gadolinium uptake
with contrast-enhanced MRI
• Microarray spots can be segmented by
combining the two different information channels
i.e. Cy3 and Cy 5).
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Information Extraction
CE MRI Tumor Segmentation
A pre- and post-contrast breast MRI image (Gd DTPA enhanced) showing a large
region of signal enhancement corresponding to invasive cancer.
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Information Extraction
CE MRI Tumor Segmentation

 K trans 
Ct 
t 
exp  k outt   exp  
trans
ve 
Vp K
 k outve 


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Dv e K trans

This diagram illustrates how the gadolinium that is injected into the
bloodstream passes through the leaky angiogenic vessel walls and
enters into the extravascular, extracellular space. This transfer constant
(Ktrans) essentially characterises vascular permeability (permeability
surface area product per unit volume of tissue).
BioMedical Informatics Lab
Digital Patient ERCIM WG Meeting, Nice
Multi-level data analysis problems: Information Extraction
Min(t)
Central
Compartment
(Blood
Plasma)
k12
kout(t)
Enhancement
CE MRI Tumor Segmentation
ROI
k21
Glandul
ar tissue
Peripheral
Component
Fat
Time(mins)
A two-compartment pharmacokinetic model with typical contrast curves for fat,
parenchymal (glandular) tissue and enhancing regions of interest. Min is the
mass of contrast injected into the blood stream with respect to time. k12 and
k21 are inter-compartment exchange rates and kout is the leaving contrast rate.
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Information Extraction
Choosing the right model…
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A)
B)
C)
D)
A), B) is a slice/rendering from a
pre-chemotherapy contrastenhanced breast MRI which
suggests (via conventional
pharmacokinetic analysis) that the
tumour is has a fairly homogenous
distribution of enhancement. C), D)
depicts the same breast after T1corrected analysis and depicts a
considerably different enhancement
characteristics – a “ring” or
peripheral enhancement. It would
appear that high T1 values in the
centre of the tumour have produced
an incorrectly high quantification of
enhancement using the standard
model (which reflects the visible
enhancement in the images).
Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Information Extraction
Microarrays
Labelling Α
Condition A
Condition
B
RNA
extraction
and labeling
Image
overlay
g1 g2
g3 g4
DNA array
Ratio
0.1 1 10
Labelling Β
Microarray imaging: An array of DNA or protein samples is
hybridized with probes to study patterns of gene expression.
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Information Extraction
A new analysis method for
extracting relevant information
Channel 1 (Ch1) and Ch2 are
combined to form the A, M
images.
Corresponding results when
using a constrained, to three
classes, segmentation.
Segmentation results using our
method with no constrains on
the number of classes (BIC
indicates 7 distinct classes in A).
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Information Extraction
the method in action…
One spot
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50 group of replicate spots
Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Normalisation
Normalization in Mammography
Tumour
Compression
Plates
1cm
Glandular
Tissue
Fatty
Tissue
Hbreast
Hint
1.0 cm
In the Hint representation, a mammogram is converted to a “height”
of non-fatty tissue.
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Normalisation
Microarrays: New challenges/problems
How to perform
efficient Image
Analysis without
losing ‘subtle’
differences in gene
expression?
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Quantification
Quantification of microcalcifications
Intensity/Height
Cthr esh

C min
C min
Cthr esh

2
 cw
A  N
  b  A ,
N

2

2 

c

N 
 w b 
, N  A
N 
A 



C min
X dimension
The variation of the perceivable contrast in the
detection of microcalcifications is suited to the local
characteristics for the adaptation of HVS using
Cmin. The classical minimal perceivable measure,
(here called Cthresh) is a global characteristic of
the mammogram and less flexible in the elimination
of FP in the detection of microcalcifications.
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Quantification
Quantitative tissue density
analysis/classification
mammogram patterns (from left to right: N1, P1, P2 and DY)
Semantics extraction
Tissue classification
K. Marias, C.P. Behrenbruch, R.P. Highnam, S. Parbhoo, A. Seifalian and Michael Brady: "A mammographic image analysis method to
detect and measure changes in breast density". European Journal of Radiology, Volume 52, Issue 3, December 2004, Pages 276-282.
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Quantification
Microarrays: denoising-quantification
What is the actual impact of image de-noising?
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Multi-level data analysis problems: Visualisation
Multimodal (MRI and X-ray) 3D visualization of 2D medical images
better highlights the necrotic centers of each tumour
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
Summary
• In order to approach the vision of the Virtual
Physiological Human, it will be essential to
develop and validate ‘individualized’, multi-level
models taking into consideration
pathophysiological information at all possible
scales.
• The extraction of useful anatomical and
physiological information from biomedical
measurements isn’t a trivial task due to the
complex physical interactions involved in each
acquisition as well as several systematic and
random errors involved in the process.
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab
The end….
http://www.ics.forth.gr/
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Digital Patient ERCIM WG Meeting, Nice
BioMedical Informatics Lab