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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 OPAL Workflow, 10 Feb 2009 3 Motivation Obstructed sleep apnea (OSA) disorder Ideally: Credit: Wikipedia OPAL Workflow, 10 Feb 2009 Caused by collapse of soft tissue walls in airway Ability to run patientspecific simulations to help diagnosis Quick and accurate method of generating model 4 OPAL Project Dynamic Modeling of the Oral, 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 Workflow, 10 Feb 2009 5 OPAL Project 3D Medical Data OPAL Workflow, 10 Feb 2009 Biomechanical Model 6 Workflow Stages 1. Imaging 2. Image processing & reconstruction 3. Reference model generation 4. Patient-specific model fitting 5. Biomechanical model OPAL Workflow, 10 Feb 2009 7 Workflow Stages 1. Imaging 2. Image processing & reconstruction 3. Reference model generation 4. Patient-specific model fitting 5. Biomechanical model OPAL Workflow, 10 Feb 2009 8 Stage 1: Imaging Structures Tongue Soft palate Hard palate Epiglottis Pharyngeal wall Airway Jaw Teeth OPAL Workflow, 10 Feb 2009 9 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… OPAL Workflow, 10 Feb 2009 10 MRI & Protocol Normal subject vs. OSA patients Control vs. treatment (appliance) OPAL Workflow, 10 Feb 2009 11 Workflow Stages 1. Imaging 2. Image processing & reconstruction 3. Reference model generation 4. Patient-specific model fitting 5. Biomechanical model OPAL Workflow, 10 Feb 2009 12 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 OPAL Workflow, 10 Feb 2009 13 Workflow Stages 1. Imaging 2. Image processing & reconstruction 3. Reference model generation 4. Patient-specific model fitting 5. Biomechanical model OPAL Workflow, 10 Feb 2009 14 Stage 3: Reference Model Generation Goal: High quality model Focus on bottom-up semi-automatic segmentation approaches eg. Livewire [3] OPAL Workflow, 10 Feb 2009 15 3D Livewire Seed points (forming contours) drawn in 2 orthogonal slice directions, and seed points automatically generated in third slice direction OPAL Workflow, 10 Feb 2009 16 Livewire Model Refinement (Claudine & Tanaya) Morphological operations Contour smoothening (active contours [4]) 3D surface reconstruction (non-parallel curve networks [5]) OPAL Workflow, 10 Feb 2009 17 Workflow Stages 1. Imaging 2. Image processing & reconstruction 3. Reference model generation 4. Patient-specific model fitting 5. Biomechanical model OPAL Workflow, 10 Feb 2009 18 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 OPAL Workflow, 10 Feb 2009 19 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 OPAL Workflow, 10 Feb 2009 20 TPS Warping, Phase 1 Patient MRI User selects a point on both patient MRI and reference model Hard to pinpoint landmarks on 3D model OPAL Workflow, 10 Feb 2009 List of corresponding points Reference Model 21 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 OPAL Workflow, 10 Feb 2009 22 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 ChanVese active contours [7] OPAL Workflow, 10 Feb 2009 Livewire 3D (~2 hours) Livewire + post processing Automated 23 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 OPAL Workflow, 10 Feb 2009 24 Workflow Stages 1. Imaging 2. Image processing & reconstruction 3. Reference model generation 4. Patient-specific model fitting 5. Biomechanical model OPAL Workflow, 10 Feb 2009 25 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 OPAL Workflow, 10 Feb 2009 26 Challenges in Segmentation 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 OPAL Workflow, 10 Feb 2009 27 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, …) OPAL Workflow, 10 Feb 2009 28 Thank you! Questions? OPAL Workflow, 10 Feb 2009 29 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. OPAL Workflow, 10 Feb 2009 30