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Proceedings Paper

Statistical fusion of continuous labels: identification of cardiac landmarks
Author(s): Fangxu Xing; Sahar Soleimanifard; Jerry L. Prince; Bennett A. Landman
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Paper Abstract

Image labeling is an essential task for evaluating and analyzing morphometric features in medical imaging data. Labels can be obtained by either human interaction or automated segmentation algorithms. However, both approaches for labeling suffer from inevitable error due to noise and artifact in the acquired data. The Simultaneous Truth And Performance Level Estimation (STAPLE) algorithm was developed to combine multiple rater decisions and simultaneously estimate unobserved true labels as well as each rater's level of performance (i.e., reliability). A generalization of STAPLE for the case of continuous-valued labels has also been proposed. In this paper, we first show that with the proposed Gaussian distribution assumption, this continuous STAPLE formulation yields equivalent likelihoods for the bias parameter, meaning that the bias parameter-one of the key performance indices-is actually indeterminate. We resolve this ambiguity by augmenting the STAPLE expectation maximization formulation to include a priori probabilities on the performance level parameters, which enables simultaneous, meaningful estimation of both the rater bias and variance performance measures. We evaluate and demonstrate the efficacy of this approach in simulations and also through a human rater experiment involving the identification the intersection points of the right ventricle to the left ventricle in CINE cardiac data.

Paper Details

Date Published: 10 March 2011
PDF: 9 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796206 (10 March 2011); doi: 10.1117/12.877884
Show Author Affiliations
Fangxu Xing, The Johns Hopkins Univ. (United States)
Sahar Soleimanifard, The Johns Hopkins Univ. (United States)
Jerry L. Prince, The Johns Hopkins Univ. (United States)
Bennett A. Landman, Johns Hopkins Univ. (United States)
Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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