Transcript OPAL Workflow & Model Generation

OPAL Workflow:
Model Generation
Tricia Pang
February 10, 2009
Motivation
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ArtiSynth [1]:
3D Biomechanical
Modeling Toolkit
Ideally:
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Model derived from
single subject source
High resolution model
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Motivation
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Obstructed sleep apnea
(OSA) disorder
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Ideally:
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Credit: Wikipedia
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Caused by collapse of
soft tissue walls in airway
Ability to run patientspecific simulations to
help diagnosis
Quick and accurate
method of generating
model
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OPAL Project
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Dynamic Modeling of the
Oral, Pharyngeal and Laryngeal (OPAL)
Complex for Biomedical Engineering
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Patient-specific modeling and model
simulation for study of OSA
Tools for clinician use in segmenting image
and importing to ArtiSynth
Come up with protocol, tools/techniques and
modifications needed for end-to-end process
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OPAL Project
3D Medical Data
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Biomechanical Model
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Workflow Stages
1. Imaging
2. Image processing & reconstruction
3. Reference model generation
4. Patient-specific model fitting
5. Biomechanical model
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Workflow Stages
1. Imaging
2. Image processing & reconstruction
3. Reference model generation
4. Patient-specific model fitting
5. Biomechanical model
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Stage 1: Imaging
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Structures
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Tongue
Soft palate
Hard palate
Epiglottis
Pharyngeal wall
Airway
Jaw
Teeth
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Data Source
MRI
Dental Appliance
w/ Markers
Cone CT of Dental Cast
Credit: Klearway, Inc.
Other:
laser scans, planar/full CT scans, tagged MRI,
ultrasound, fluoroscopy, cadaver data…
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MRI & Protocol
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Normal subject vs. OSA patients
Control vs. treatment (appliance)
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Workflow Stages
1. Imaging
2. Image processing & reconstruction
3. Reference model generation
4. Patient-specific model fitting
5. Biomechanical model
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Stage 2: Image processing &
Reconstruction
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N3 correction [2]
(Non-parametric non-uniform intensity normalization)
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Cropping
Cubic interpolation
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Image registration & reconstruction (Bruno’s work)
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Combining 3 data sets → high-quality data set
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Workflow Stages
1. Imaging
2. Image processing & reconstruction
3. Reference model generation
4. Patient-specific model fitting
5. Biomechanical model
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Stage 3:
Reference Model Generation
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Goal: High quality model
Focus on bottom-up semi-automatic
segmentation approaches
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eg. Livewire [3]
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3D Livewire
Seed points (forming contours)
drawn in 2 orthogonal slice
directions, and seed points
automatically generated in third
slice direction
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Livewire Model
Refinement
(Claudine & Tanaya)
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Morphological
operations
Contour smoothening
(active contours [4])
3D surface
reconstruction
(non-parallel curve
networks [5])
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Workflow Stages
1. Imaging
2. Image processing & reconstruction
3. Reference model generation
4. Patient-specific model fitting
5. Biomechanical model
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Stage 4:
Patient-Specific Model Generation
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Goal: Accurate model, generated with
minimal user interaction
Focus on top-down or automated approaches
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Morphological warping operations
Deformable model crawlers
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Thin-Plate Spline Warping
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Thin-plate spline (TPS) deformation [6]:
interpolating surfaces over a set of landmarks
based on linear and affine-free local
deformation
Reference
Model
Warp Result
Warp field
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TPS Warping, Phase 1
Patient MRI
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User selects a
point on both
patient MRI
and reference
model
Hard to
pinpoint
landmarks on
3D model
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List of
corresponding
points
Reference Model
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TPS Warping, Phase 2
Reference MRI
(has a pre-built
3D model)
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Predefined
landmarks shown
on reference
MRI, user selects
equivalent point
on patient MRI
Can be improved
by automated
point-matching
Patient MRI
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Chan-Vese Active Contours
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Highly automated
method
Combine 2D
segmentation of axial
slices in Matlab
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User-indicated start point
Iterate sequentially using
previous segmentation as
starting contour for ChanVese active contours [7]
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Livewire 3D
(~2 hours)
Livewire +
post processing
Automated
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AC on axial
(2 minutes)
Deformable Organism Crawler
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Automatically segment airway by growing a
tubular organism, guided by image data and a
priori anatomical knowledge
Developed in I-DO toolkit [8]
Advantages:
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Analysis and labeling capabilities
Ability to incorporate shape-based
prior knowledge
Modular hierarchical development framework
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Workflow Stages
1. Imaging
2. Image processing & reconstruction
3. Reference model generation
4. Patient-specific model fitting
5. Biomechanical model
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Stage 5:
Biomechanical Model
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Import surface mesh into ArtiSynth
Work in progress
Challenges:
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Determining “rest” position from inverse modeling
Defining interior nodes and muscle end points
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Challenges in
Segmentation
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Medical image data quality
Bottom-up methods: Need for general
procedure and abstraction from anatomy
being segmented
Top-down methods: Need good atlas model
Validation with gold standard segmentation
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Future Directions in
Segmentation
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Deformable organism crawler
Automated morphing of reference model into
patient model
Additions to Livewire
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Oblique slices
Sub-pixel resolution
Convert to graphics implementation
Add smoothness by regularization
(eg. by spline, a priori model, …)
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Thank you!
Questions?
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References
[1] Fels, S., Vogt, F., van den Doel, K., Lloyd, J., Stavness, I., and Vatikiotis-Bateson, E. Developing
Physically-Based, Dynamic Vocal Tract Models using ArtiSynth. Proc. Int. Seminar Speech Production
(2006), 419-426.
[2] Sled, G., Zijdenbos, A. P., and Evans, A. C. Non-parametric method for automatic correction of intensity
nonuniformity in MRI data. IEEE Trans. in Medical Imaging
17, 1 (1998), 87-97.
[3] Poon, M., Hamarneh, G., and Abugharbieh, R. Effcient interactive 3d livewire segmentation of complex
objects with arbitrary topology. Comput. Med Imaging and Graphics (2009), in press.
[4] Hamarneh, G., Chodorowski, A., and Gustavsson, T. Active Contour Models: Application to Oral Lesion
Detection in Color Images. IEEE International Conference on Systems, Man, and Cybernetics 4
(2000), 2458 -2463.
[5] Liu, L., Bajaj, C., Deasy, J. O., Low, D. A., and Ju, T. Surface reconstruction from non-parallel curve
networks. Eurographics 27, 2 (2008), 155-163.
[6] Bookstein, F. L. Principal Warps: Thin-Plate Splines and the Decomposition of Deformations. IEEE
Transactions on Pattern Analysis and Machine Intelligence 11, 6 (1989), 567-585.
[7] Chan, T., and Vese, L. Active contours without edges. IEEE Transactions on Image Processing 10, 2
(2001), 266-277.
[8] McIntosh, C. and Hamarneh, G. I-DO: A “Deformable Organisms” framework for ITK. Medical Image
Analysis Lab, SFU. Release 0.50.
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