Robust segmentation using non-parametric snakes with multiple cues for applications in radiation oncology

Jayashree Kalpathy-Cramer; Umut Ozertem; William Hersh; Martin Fuss; Deniz Erdogmus

doi:10.1117/12.812712

27 March 2009 Robust segmentation using non-parametric snakes with multiple cues for applications in radiation oncology

Jayashree Kalpathy-Cramer, Umut Ozertem, William Hersh, Martin Fuss, Deniz Erdogmus

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72594S (2009) https://doi.org/10.1117/12.812712
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Radiation therapy is one of the most effective treatments used in the treatment of about half of all people with cancer. A critical goal in radiation therapy is to deliver optimal radiation doses to the perceived tumor while sparing the surrounding healthy tissues. Radiation oncologists often manually delineate normal and diseased structures on 3D-CT scans, a time consuming task. We present a segmentation algorithm using non-parametric snakes and principal curves that can be used in an automatic or semi-supervised fashion. It provides fast segmentation that is robust with respect to noisy edges and does not require the user to optimize a variety of parameters, unlike many segmentation algorithms. It allows multiple cues to be incorporated easily for the purposes of estimating the edge probability density. These cues, including texture, intensity and shape priors, can be used simultaneously to delineate tumors and normal anatomy, thereby increasing the robustness of the algorithm. The notion of principal curves is used to interpolate between data points in sparse areas. We compare the results using a non-parametric snake technique with a gold standard consisting of manually delineated structures for tumors as well as normal organs.

Citation Download Citation

Jayashree Kalpathy-Cramer, Umut Ozertem, William Hersh, Martin Fuss, and Deniz Erdogmus "Robust segmentation using non-parametric snakes with multiple cues for applications in radiation oncology", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72594S (27 March 2009); https://doi.org/10.1117/12.812712

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