A probabilistic model for predicting diameters of lung airways

Spencer Yuen; Matthew Brown; Sumit Shah; Hyun Jun Kim; Sarinnapha Vasunilashorn; Eric Kleerup; Jonathan Goldin M.D.

doi:10.1117/12.595710

29 April 2005 A probabilistic model for predicting diameters of lung airways

Spencer Yuen, Matthew Brown, Sumit Shah, Hyun Jun Kim, Sarinnapha Vasunilashorn, Eric Kleerup, Jonathan Goldin M.D.

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.595710
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

The accurate characterization of pulmonary airways on CT is potentially very useful for diagnosis and evaluation of lung diseases. The task is challenging due to their small size and variable orientation. We propose a probabilistic modeling technique and a set of measurement tools to quantitate airway morphology. We extract the airway tree structure from high resolution CT scans with a seeded region growing algorithm. Individual airway branches are identified by reducing the airway tree to a set of central axes. Properties such as lumen diameter and branch angle are measured from these central axes. The structure of the Bayesian model is inferred from a set of equations representing the parent-daughter relationships between branches, such as equations of air flow ratio and flow conservation. The CT measurements are used to instantiate the conditional probability tables of the Bayesian model. To evaluate the model, it was used to predict the airway diameter for the 2nd, 3rd, 4th, 5th, and 6th generations of the airway tree. We show that the model can reasonably predict the diameter of a particular airway branch, given information of its parent.

Citation Download Citation

Spencer Yuen, Matthew Brown, Sumit Shah, Hyun Jun Kim, Sarinnapha Vasunilashorn, Eric Kleerup, and Jonathan Goldin M.D. "A probabilistic model for predicting diameters of lung airways", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.595710

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available