opal workflow: model generation
DESCRIPTION
OPAL Workflow: Model Generation. Tricia Pang February 10, 2009. Motivation. ArtiSynth [1]: 3D Biomechanical Modeling Toolkit Ideally: Model derived from single subject source High resolution model. Motivation. Obstructed sleep apnea (OSA) disorder - PowerPoint PPT PresentationTRANSCRIPT
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OPAL Workflow: Model Generation
Tricia Pang
February 10, 2009
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Motivation
ArtiSynth [1]:3D Biomechanical Modeling Toolkit
Ideally: Model derived from
single subject source High resolution model
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Motivation
Obstructed sleep apnea (OSA) disorder Caused by collapse of
soft tissue walls in airway Ideally:
Ability to run patient-specific simulations to help diagnosis
Quick and accurate method of generating modelCredit: Wikipedia
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OPAL Project
Dynamic Modeling of theOral, Pharyngeal and Laryngeal (OPAL)Complex for Biomedical Engineering 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 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
Structures Tongue Soft palate Hard palate Epiglottis Pharyngeal wall Airway Jaw Teeth
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Data SourceMRI
Credit: Klearway, Inc.
Dental Appliancew/ Markers Cone CT of Dental Cast
Other:laser scans, planar/full CT scans, tagged MRI, ultrasound, fluoroscopy, cadaver data…
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MRI & Protocol
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
N3 correction [2](Non-parametric non-uniform intensity normalization)
Cropping Cubic interpolation
Image registration & reconstruction (Bruno’s work)
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
Goal: High quality model Focus on bottom-up semi-automatic
segmentation approaches 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 ModelRefinement
Morphological operations
Contour smoothening(active contours [4])
3D surface reconstruction(non-parallel curve networks [5])
(Claudine & Tanaya)
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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
Goal: Accurate model, generated with minimal user interaction
Focus on top-down or automated approaches Morphological warping operations Deformable model crawlers
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Thin-Plate Spline Warping
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
Reference Model
List of corresponding points
User selects a point on both patient MRI and reference model
Hard to pinpoint landmarks on 3D model
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TPS Warping, Phase 2Reference MRI (has a pre-built 3D model)
Patient MRI
Predefined landmarks shown on reference MRI, user selects equivalent point on patient MRI
Can be improved by automated point-matching
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Chan-Vese Active Contours
Highly automated method
Combine 2D segmentation of axial slices in Matlab User-indicated start point Iterate sequentially using
previous segmentation as starting contour for Chan-Vese active contours [7]
Livewire 3D(~2 hours)
Livewire +post processing
Automated AC on axial(2 minutes)
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Deformable Organism Crawler
Automatically segment airway by growing a tubular organism, guided by image data and a priori anatomical knowledge
Developed in I-DO toolkit [8] Advantages:
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
Import surface mesh into ArtiSynth Work in progress Challenges:
Determining “rest” position from inverse modeling Defining interior nodes and muscle end points
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Challenges inSegmentation
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
Deformable organism crawler Automated morphing of reference model into
patient model Additions to Livewire
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 Imaging17, 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.