OPAL Workflow Model Generation Tricia Pang February 10

OPAL Workflow: Model Generation Tricia Pang February 10, 2009

Motivation l l Arti. Synth [1]: 3 D Biomechanical Modeling Toolkit Ideally: l l Model derived from single subject source High resolution model OPAL Workflow, 10 Feb 2009 3

Motivation l Obstructed sleep apnea (OSA) disorder l l Ideally: l l 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 l Dynamic Modeling of the Oral, Pharyngeal and Laryngeal (OPAL) Complex for Biomedical Engineering l l l Patient-specific modeling and model simulation for study of OSA Tools for clinician use in segmenting image and importing to Arti. Synth Come up with protocol, tools/techniques and modifications needed for end-to-end process OPAL Workflow, 10 Feb 2009 5

OPAL Project 3 D 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 l Structures l l l l 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 l l 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 l N 3 correction [2] (Non-parametric non-uniform intensity normalization) l Cropping Cubic interpolation l Image registration & reconstruction (Bruno’s work) l l 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 l l Goal: High quality model Focus on bottom-up semi-automatic segmentation approaches l eg. Livewire [3] OPAL Workflow, 10 Feb 2009 15

3 D 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) l l l Morphological operations Contour smoothening (active contours [4]) 3 D 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 l l Goal: Accurate model, generated with minimal user interaction Focus on top-down or automated approaches l l Morphological warping operations Deformable model crawlers OPAL Workflow, 10 Feb 2009 19

Thin-Plate Spline Warping l 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 l l User selects a point on both patient MRI and reference model Hard to pinpoint landmarks on 3 D model OPAL Workflow, 10 Feb 2009 List of corresponding points Reference Model 21

TPS Warping, Phase 2 Reference MRI (has a pre-built 3 D model) l l 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 l l Highly automated method Combine 2 D segmentation of axial slices in Matlab l l User-indicated start point Iterate sequentially using previous segmentation as starting contour for Chan. Vese active contours [7] OPAL Workflow, 10 Feb 2009 Livewire 3 D (~2 hours) Livewire + post processing Automated 23 AC on axial (2 minutes)

Deformable Organism Crawler l l l Automatically segment airway by growing a tubular organism, guided by image data and a priori anatomical knowledge Developed in I-DO toolkit [8] Advantages: l l l 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 l Import surface mesh into Arti. Synth Work in progress Challenges: l l Determining “rest” position from inverse modeling Defining interior nodes and muscle end points OPAL Workflow, 10 Feb 2009 26

Challenges in Segmentation l l 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 l l l Deformable organism crawler Automated morphing of reference model into patient model Additions to Livewire l l 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 Arti. Synth. 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 3 d 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] Mc. Intosh, 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