OPAL Workflow: Model Generation

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 • 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 model Credit: 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

Stage 1: Imaging • Structures • Tongue • Soft palate • Hard palate • Epiglottis • Pharyngeal wall • Airway • Jaw • Teeth

Data Source Dental Appliancew/ Markers Cone CT of Dental Cast MRI Credit: Klearway, Inc. Other:laser scans, planar/full CT scans, tagged MRI, ultrasound, fluoroscopy, cadaver data…

MRI & Protocol • Normal subject vs. OSA patients • Control vs. treatment (appliance)

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

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 (Claudine & Tanaya) • Morphological operations • Contour smoothening(active contours [4]) • 3D surface reconstruction(non-parallel curve networks [5])

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 • User selects a point on both patient MRI and reference model • Hard to pinpoint landmarks on 3D model Patient MRI List of corresponding points Reference Model

TPS Warping, Phase 2 Reference MRI (has a pre-built 3D model) • Predefined landmarks shown on reference MRI, user selects equivalent point on patient MRI • Can be improved by automated point-matching Patient MRI

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] Automated AC on axial(2 minutes) Livewire 3D (~2 hours) Livewire +post processing

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-basedprior knowledge • Modular hierarchical development framework

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.

OPAL Workflow: Model Generation