Robust optic nerve segmentation on clinically acquired computed tomography

Robert L. Harrigan; Swetasudha Panda; Andrew J. Asman; Katrina M. Nelson; Shikha Chaganti; Michael P. DeLisi; Benjamin C. W. Yvernault; Seth A. Smith; Robert L. Galloway; Louise A. Mawn; Bennett A. Landman

doi:10.1117/1.JMI.1.3.034006

17 December 2014 Robust optic nerve segmentation on clinically acquired computed tomography

Robert L. Harrigan, Swetasudha Panda, Andrew J. Asman, Katrina M. Nelson, Shikha Chaganti, Michael P. DeLisi, Benjamin C. W. Yvernault, Seth A. Smith, Robert L. Galloway, Louise A. Mawn, Bennett A. Landman

Author Affiliations +

Journal of Medical Imaging, Vol. 1, Issue 3, 034006 (December 2014). https://doi.org/10.1117/1.JMI.1.3.034006

Abstract

The optic nerve (ON) plays a critical role in many devastating pathological conditions. Segmentation of the ON has the ability to provide understanding of anatomical development and progression of diseases of the ON. Recently, methods have been proposed to segment the ON but progress toward full automation has been limited. We optimize registration and fusion methods for a new multi-atlas framework for automated segmentation of the ONs, eye globes, and muscles on clinically acquired computed tomography (CT) data. Briefly, the multi-atlas approach consists of determining a region of interest within each scan using affine registration, followed by nonrigid registration on reduced field of view atlases, and performing statistical fusion on the results. We evaluate the robustness of the approach by segmenting the ON structure in 501 clinically acquired CT scan volumes obtained from 183 subjects from a thyroid eye disease patient population. A subset of 30 scan volumes was manually labeled to assess accuracy and guide method choice. Of the 18 compared methods, the ANTS Symmetric Normalization registration and nonlocal spatial simultaneous truth and performance level estimation statistical fusion resulted in the best overall performance, resulting in a median Dice similarity coefficient of 0.77, which is comparable with inter-rater (human) reproducibility at 0.73.

Citation Download Citation

Robert L. Harrigan, Swetasudha Panda, Andrew J. Asman, Katrina M. Nelson, Shikha Chaganti, Michael P. DeLisi, Benjamin C. W. Yvernault, Seth A. Smith, Robert L. Galloway, Louise A. Mawn, and Bennett A. Landman "Robust optic nerve segmentation on clinically acquired computed tomography," Journal of Medical Imaging 1(3), 034006 (17 December 2014). https://doi.org/10.1117/1.JMI.1.3.034006

Published: 17 December 2014

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