OPAL Workflow: Model Generation

OPAL Workflow: Model Generation

Tricia Pang

February 10, 2009

OPAL Workflow, 10 Feb 2009 3

Motivation

ArtiSynth [1]:3D Biomechanical Modeling Toolkit

Ideally: Model derived from

single subject source High resolution model


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


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


OPAL Project

3D Medical Data Biomechanical Model


Workflow Stages

1. Imaging

2. Image processing & reconstruction

3. Reference model generation

4. Patient-specific model fitting

5. Biomechanical model


Workflow Stages

1. Imaging






Stage 1: Imaging

Structures Tongue Soft palate Hard palate Epiglottis Pharyngeal wall Airway Jaw Teeth


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…


MRI & Protocol

Normal subject vs. OSA patients Control vs. treatment (appliance)


Workflow Stages

1. Imaging






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


Workflow Stages

1. Imaging






Stage 3:Reference Model Generation

Goal: High quality model Focus on bottom-up semi-automatic

segmentation approaches eg. Livewire [3]


3D Livewire

Seed points (forming contours) drawn in 2 orthogonal slice directions, and seed points automatically generated in third slice direction


Livewire ModelRefinement

Morphological operations

Contour smoothening(active contours [4])

3D surface reconstruction(non-parallel curve networks [5])

(Claudine & Tanaya)


Workflow Stages

1. Imaging






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


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


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


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


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)


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


Workflow Stages

1. Imaging






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


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


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, …)


Thank you!

Questions?


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.

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OPAL Workflow: Model Generation