PREGNANT WOMAN NUMERICAL MO - Telecom Paris

Numerical model of the pregnant woman based on

medical Images

Jérémie Anquez, Elsa Angelini, Isabelle Bloch (CNRS UMR 5141 LTCI, ENST Paris)

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Outline

1. EMFs influence on human body2. Pregnant woman model3. Uterus/Fetus segmentation

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1. EMFs influence on human body

EMFs represent one of the most common and fastest growing

environmental influences, about which anxiety and speculation are spreading

Examples of FAQ on WHO website:

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1. EMFs influence on human body

The “International EMF Project” has been launched in 1996 to assess the scientific evidence of possible health

effects of EMF

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1. EMFs influence on human body

Different areas involved in the assessment of EMF influence on health

Biology (studies on cells, on animals…)Physics (measures on physical phantoms and numerical models)Mathematics/Informatics(elaboration of models)

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Equipment certification : phantoms are used for in situ Specific Absorption Rate (SAR) measurements. SAR is the rate at which RF energy is imparted to a

unit mass of a biological body

ρσ 2E.

SAR =ρ

E

σElectric field strength in the tissue

Conductivity of the tissue

Mass density of the tissue

1. EMFs influence on human body

(W.kg-1)

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2

3

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1. EMFs influence on human body

Anthropomorphic models are used for simulated SAR measurements. Two different

kinds of models exist:

Stylized models

VS

Voxelized models

Advantages:

• Scalable

• Easy to morph

Drawbacks:

• Simplification of human

anatomy (shape,

variability…)

Advantages:

• Accurate representation

of human anatomy

Drawbacks:

• Patient dependant

• Difficult to morph

• Time consuming task

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Anthropomorphic models are used for simulated SAR measurements

1. EMFs influence on human body

SAR

E2

ρσ

Anthropomorphic

model

Simulated electric

field

ρσ 2E.

SAR =

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2. Pregnant woman modeling

• Pregnant woman models needed• Available models• Hybrid model description

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2. Pregnant woman modeling

1. Studies on EMF exposure of the adult

“Mobile Communications and Biology”, COMOBIO project [1999-2002]

2. Studies on EMF exposure of the children

“Dosimetric analysis of 3rd generation mobile phones ”, ADONIS project [2003-2006]

3. Studies on EMF exposure of the pregnant woman

WHO priority : need for adequate models

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Existing real-shaped phantom: abdomen composed of three layers, mother’s body,

amniotic fluid and fetus (Kawai [2005]).

2. Pregnant woman modeling

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Existing stylized models (Stabin [1995]; Kainz [2003])

Surface equations CAD model

2. Pregnant woman modeling

13Segmentation of CT data

Existing voxelized model (Cech [2005]; Xu [2004])

2. Pregnant woman modeling

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Uterus/Fetus model: coarse fetus model due to large spatial spacing in CT scan [Xu]

No detailed fetus models available

2. Pregnant woman modeling

Resolution :

1 x 1 x 10 mm3

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Designing the pregnant woman model from a whole-body acquisition is challenging

• Uterus pressure on patient’s cave vena

Need to assemble a hybrid model including:

• a Uterus/Fetus voxelized model (MRI, US)

• a stylized woman body (coarse body shape including skin, muscle and fat information)

2. Pregnant woman modeling

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Stylized

model

Voxelized

model

Hybrid model

2. Pregnant woman modeling

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3. Uterus/Fetus segmentation

Available data for uterus/fetus segmentation:• MRI data (Saint Vincent de Paul hospital): good

image quality but forbidden before 22 GW

• Ultrasound data (Philips Medical Systems): weak image quality but authorized anytime

Modalities used for differents gestational ages

Gestational

age (weeks)

Number of

Cases

40302010

US MRI

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Uterus/fetus segmentation based on MRI data (Cochin hospital partership)

• Large field of view

• Good overall contrast

• High resolution

MRI sequence requirements:

• Ultra-fast acquisition

• Little sensitivity to motion artifacts

3. Uterus/Fetus segmentation

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3. Uterus/Fetus segmentation

Motion related artifacts

Coarse resolutionLow overall contrast

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3. Uterus/Fetus segmentation

True FISP (Siemens) / Fiesta (GE) satisfies all requirements…

… but inhomogeneity correction needed

Slice

10/30

Slice

15/30

Slice

20/30

1

30

130

Resolution (mm3):

0.8 x 0.8 x 3

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3. Uterus/Fetus segmentation

Poor results using usual methods

• Brain inhomogeneity correction oriented

• User interaction needed (Dawant [1993])

• High computational cost (Zhang [2001])

• Parameters tuning needed (Mangin [2000])

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3. Uterus/Fetus segmentation

Segmentation using basic tools to study tissues intensity

Segmentation result 3D reconstruction

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3. Uterus/Fetus segmentation

Torso antenna generate a quasi parallel magnetic field

B1

Patient

Upper torso coil

Lower torso coil

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3. Uterus/Fetus segmentation

Tissues intensity mean and standard deviation trend in Z (slice acquisition direction)

Brain LungsUrinary

bladder

Intra-uterine

tissuesSame decreasing trend in Z

for every tissues : similar

trend for intra-uterine

tissues

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3. Uterus/Fetus segmentation

Hypothesis: • Intensity variation depends on Z only

• Same gray level distribution in any slice

Correction based on a surface fitting like method

kµ : kth slice mean

k

kji

kji

ss

µ,,'

,, =

Uterus

segmentation on

T2 Thick slab

Uterus extraction

on Fiesta data

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3. Uterus/Fetus segmentation

Inhomogeneity correction influence on intra-uterine tissues

Corrected dataOriginal data

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3. Uterus/Fetus segmentation

Inhomogeneity

correction

Corrected data

Original data

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3. Uterus/Fetus segmentation

Brain LungsUrinary

bladder

Inhomogeneity

correction

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3. Uterus/Fetus segmentation

The standard deviation of the intensity in 3D is lessened in every tissue

Advantages: • Easy implementation

• Very fast

• Robust

Drawbacks: • Variations in X and Y not considered

• Gray level distribution of intra-uterine tissues is not strictly equal from one slice to another

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3. Uterus/Fetus segmentation

Starting point

• Sequential segmentation with anatomical spatial relationship

• Complex scene with many different structures

• Need for a starting point eyes

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3. Uterus/Fetus segmentation

Eyes characteristics

• High intensity and good contrast with surrounding tissues

• Spherical morphology

• Weak variability (diameter, number…)

• Small organ (Z resolution)

X

Y

Z

Y

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3. Uterus/Fetus segmentation

Eyes detection algorithm

• Binary objects extraction using thresholding and connex component

• Object list reduction considering the objects volume or surface

• Object pair detection computing a score based on shape and distance between objects

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3. Uterus/Fetus segmentation

Threshold consequences

Must be high enough to disconnect the eyes from close structures but not too high, not to exclude peripheral voxels, and to preserve the objects spherical shape

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3. Uterus/Fetus segmentation

Threshold consequences (2)

Even using the second mode value as threshold, connections to the brain remain in 3D because of partial volume effect

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3. Uterus/Fetus segmentation

Threshold consequences (3)• Disconnection with morphological opening spoils the object shape

• Partial volume artifact has no influence on center slice

Eye median slice localization

X

Y

Z

Y

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3. Uterus/Fetus segmentation

Threshold, object extraction & object list reduction

VVV *3.1*7.0 ≤≤ with zyxRV ∆∆∆Π= .)..).(( 2

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3. Uterus/Fetus segmentation

Best object pair extraction• Compactness computation for each object

( ) ( )( )2..4OiS

OiVOiC Π=

• Measure computation for each object pair, based on distance between objects center

)),((.)(.)(),( jijiji ccdOCOCOOS µ=

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3. Uterus/Fetus segmentation

Final result

Good results on 10 datasets

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3. Uterus/Fetus segmentation

Median sagittal plane reconstruction using the eyes

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3. Uterus/Fetus segmentation

Brain detection on median sagittal plane…

… using a close neighborhood

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3. Uterus/Fetus segmentation

• Brain and spine segmentation to set fetus global position

• Segmentation of abdominal organs

• Segmentation of the head and the torso

• Challenging : segmentation of the limbs

Fetus

Uterus• Different objects to segment: placenta, amniotic fluid,

umbilical cord and uterus walls

Future works

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Uterus/fetus segmentation based on ultrasound data

3. Uterus/Fetus segmentation

Deformable model with statistical measures:

• Initialization

• Image-based information

• Implementation

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3. Uterus/Fetus segmentation

Segmentation of 3D ultrasound data

• Tool: 3D Slicer open source software (www.slicer.org)

• Segmentation based on basic operations: thresholding, manual contouring.

• Smoothing with mathematical morphology operators.

• Coarse segmentation: final model includes maternal tissues, amniotic fluid and fetal tissues

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3. Uterus/Fetus segmentation

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Merge surface model segmented and image information to provide a good initialization

• Tool: Blender open source software (www.blender.org)

• Determine a set of visual landmarks corresponding to the key articulations in the data (neck, pelvis, elbow…).

• Morph the model’s articulations towards the landmarks

3. Uterus/Fetus segmentation

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Fetus model positioning

3. Uterus/Fetus segmentation

Mesh morphing using Blender

with definition of an associated

armature

Posing the

armature

morphes mesh

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3. Uterus/Fetus segmentation

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3. Uterus/Fetus segmentation

Deformable model with statistical measures

• Levelset-based method using a priori knowledge of statistical distribution of grey levels in ultrasound data

• To model gray level behavior of ultrasound images, the Rayleigh distribution is classically considered (Bovik [1988], Chesnaud [1999])

Preliminary work : validation of the Rayleigh distribution on the data

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3. Uterus/Fetus segmentation

The Rayleigh distribution fits well when we consider the whole fetus…

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3. Uterus/Fetus segmentation

…but the body and the head have really different distributions

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3. Uterus/Fetus segmentation

Exponential distribution suits better for maternal tissues…

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3. Uterus/Fetus segmentation

…and perfectly for amniotic fluid intensities

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3. Uterus/Fetus segmentation

Preliminary studies using level set method based on those distributions are in progress

Segmentation

result

Original data

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• Implementation of an automatic detection of landmarks in data before morphing (head, spine, elbows, knees…) to initialize the model

• Validation of the statistic distributions observed, studying other data

• Evaluation of the method robustness at uterus/fetus interface

• Segmentation of fetal organs

3. Uterus/Fetus segmentation

Future works

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Pregnant woman model generation

• Methodology to apply to merge the uterus/fetus model and the woman stylized model

• Merging of the uterus fetus/model in an existing woman voxelized model

• Model morphing (simulation of different positions)

• Dosimetric studies when the model is exposed to different EMF sources; utilization in different applications (car accidents, photon exposure…)

Conclusion

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QUESTIONS ?