Multiatlas segmentation as nonparametric regression

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Publication Type pre-print
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Creator Awate, Suyash Prakash
Other Author Whitaker, Ross T.
Title Multiatlas segmentation as nonparametric regression
Date 2014-01-01
Description This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation.We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator.We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Volume 33
Issue 9
First Page 1803
Last Page 1817
Language eng
Bibliographic Citation Awate, S. P., & Whitaker, R. T. (2014). Multiatlas segmentation as nonparametric regression. IEEE Transactions on Medical Imaging, 33(9), 1803-17.
Rights Management (c) 2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 3,443,843 bytes
Identifier uspace,18936
ARK ark:/87278/s65b3bkd
Setname ir_uspace
Date Created 2014-09-22
Date Modified 2014-09-23
ID 712679
Reference URL https://collections.lib.utah.edu/ark:/87278/s65b3bkd
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