Improving cerebellar segmentation with statistical fusion

Andrew J. Plassard; Zhen Yang; Swati Rane; Jerry L. Prince; Daniel O. Claassen; Bennett A. Landman

doi:10.1117/12.2216966

21 March 2016 Improving cerebellar segmentation with statistical fusion

Andrew J. Plassard, Zhen Yang, Swati Rane, Jerry L. Prince, Daniel O. Claassen, Bennett A. Landman

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97842R (2016) https://doi.org/10.1117/12.2216966
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

The cerebellum is a somatotopically organized central component of the central nervous system well known to be involved with motor coordination and increasingly recognized roles in cognition and planning. Recent work in multi-atlas labeling has created methods that offer the potential for fully automated 3-D parcellation of the cerebellar lobules and vermis (which are organizationally equivalent to cortical gray matter areas). This work explores the trade offs of using different statistical fusion techniques and post hoc optimizations in two datasets with distinct imaging protocols. We offer a novel fusion technique by extending the ideas of the Selective and Iterative Method for Performance Level Estimation (SIMPLE) to a patch-based performance model. We demonstrate the effectiveness of our algorithm, Non-Local SIMPLE, for segmentation of a mixed population of healthy subjects and patients with severe cerebellar anatomy. Under the first imaging protocol, we show that Non-Local SIMPLE outperforms previous gold-standard segmentation techniques. In the second imaging protocol, we show that Non-Local SIMPLE outperforms previous gold standard techniques but is outperformed by a non-locally weighted vote with the deeper population of atlases available. This work advances the state of the art in open source cerebellar segmentation algorithms and offers the opportunity for routinely including cerebellar segmentation in magnetic resonance imaging studies that acquire whole brain T1-weighted volumes with approximately 1 mm isotropic resolution.

Citation Download Citation

Andrew J. Plassard, Zhen Yang, Swati Rane, Jerry L. Prince, Daniel O. Claassen, and Bennett A. Landman "Improving cerebellar segmentation with statistical fusion", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842R (21 March 2016); https://doi.org/10.1117/12.2216966

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