Tricia Pang November 25, 2008

Tricia PangNovember 25, 2008

Three-Dimensional Human Airway Segmentation for Sleep Apnea Diagnosis

using Tubular Deformable Organisms

Motivation Approach

Preliminary Investigation Deformable Organisms

Preliminary Results Conclusion

OVERVIEW

Motivation Obstructed sleep apnea

(OSA) disorder Caused by collapse of soft

tissue walls in the airway → model patient's airway to help diagnosis

Hand-segmentation: laborious

Goal: to develop automated tool for creating a patient-specific model of the airway

Credit: Wikipedia

Motivation Artisynth [2] &

OPAL Project(OPAL = Dynamic Modeling of the Oral, Pharyngeal and Laryngeal Complex for Biomedical Engineering) Import resulting

airway into dynamic throat and mouth model for simulation

Motivation Approach



OVERVIEW

Data Source - MRI

Normal subjects, OSA patients, various treatments Volumetric and cross-sectional measurements

Motivation Approach



OVERVIEW

Preliminary Investigation

Combined 2D segmentation of axial slices in Matlab Procedure:

User-indicated start point at base of airway Starting on axial slice at start point, grow ellipse outward Iterate on all axial slices moving upwards along airway,

and use previous segmentation as starting contour “Active contours without edges” (Chan-Vese) [1]:

Based on Mumford-Shah framework Evolve curve by minimizing energy from image

(interior/exterior mean) and curvature

Motivation Approach



OVERVIEW

Deformable Organism

I-DO: framework for ITK (McIntosh & Hamarneh) [4] Geometrical and physical layers of classical deformable

models (data-driven) Behavioral and cognitive layers for intelligent deformation

control (knowledge-driven) Related work:

Spinal crawler [5] Vessel crawler [6]

Deformable Organism

Goal: automatically segment airway by growing a tubular organism, guided by image data and a priori anatomical knowledge

Advantages: Increased accuracy Analysis and labeling capabilities Ability to incorporate shape-based

prior knowledge Modular framework

Deformable Organism Framework

Summary of LayersControl Center

Grow, terminate, (branch)

BehaviorGrow, fit, (branch)

Physics/DeformationSpring-mass system

Medial and boundary nodesRadial, circumferential and sheer springs

GeometricMedial-based shape representation

Tubular with symmetric cross-section (often elliptical)

Sensors‘GrowSense’

‘HessianSense’(‘BranchSense’)

Viewer Adaptor

Graphical interface for viewing geometry of DOs and their deformations in real time

Motivation Approach



OVERVIEW

Summary

Model of a patient’s airway valuable to diagnosing the OSA disorder

Tubular deformable organisms spring-mass system initiated at a user-indicated point grown along the airway boundary using a priori

knowledge of upper airway anatomy

References[1] Chan, T. and Vese L. Active Contours Without Edges. IEEE Transactions

on Image Processing, 10 (2001)[2] Fels, S., et al. Artisynth: A biomechanical simulation platform for the

vocal tract and upper airway. International Seminar on Speech Production (2006)

[3] Hamarneh, G. and McIntosh, C. Physics-Based Deformable Organisms for Medical Image Analysis. Proc of SPIE 5747 (2005) 326-335

[4] McIntosh, C. and Hamarneh, G. I-DO: A “Deformable Organisms” framework for ITK. Medical Image Analysis Lab, SFU. Release 0.50.

[5] McIntosh, C. and Hamarneh, G. Spinal Crawlers: Deformable Organisms for Spinal Cord Segmentation and Analysis. MICCAI (2006) 808–815

[6] McIntosh, C. and Hamarneh, G. Vessel Crawlers: 3D Physically-based Deformable Organisms for Vasculature Segmentation and Analysis. Proceedings of IEEE CVPR (2006)

Thank you!

Questions?

Tricia Pang November 25, 2008

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