A novel right ventricle segmentation strategy using local spatio-temporal MRI information with a prior regularizer term

Angélica Atehortúa; Fabio Martínez; Eduardo Romero

doi:10.1117/12.2035512

19 November 2013 A novel right ventricle segmentation strategy using local spatio-temporal MRI information with a prior regularizer term

Angélica Atehortúa, Fabio Martínez, Eduardo Romero

Author Affiliations +

Proceedings Volume 8922, IX International Seminar on Medical Information Processing and Analysis; 892203 (2013) https://doi.org/10.1117/12.2035512
Event: IX International Seminar on Medical Information Processing and Analysis, 2013, Mexico City, Mexico

Abstract

In this work is presented a novel strategy that tracks the right ventricle (RV) shape during a whole cardiac cycle in magnetic resonance sequences (MRC). The proposed approach obtains a set of spatio-temporal observations from a bidirectional per pixel motion descriptor which are each time fused with prior learned edges. A main advantage of the proposed approach is a robust MRI heart characterization that is regularized by a prior information, obtaining in each cardiac state coherent results. The proposed approach achieves a Dice Score of 0.64 evaluated over 16 patients.

Citation Download Citation

Angélica Atehortúa, Fabio Martínez, and Eduardo Romero "A novel right ventricle segmentation strategy using local spatio-temporal MRI information with a prior regularizer term", Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 892203 (19 November 2013); https://doi.org/10.1117/12.2035512

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