Record Details

Multiatlas Segmentation as Nonparametric Regression

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Title Multiatlas Segmentation as Nonparametric Regression
 
Creator AWATE, SP
WHITAKER, RT
 
Subject k-nearest-neighbor (kNN)
label fusion
multi-atlas
nonparametric
regression
segmentation
IMAGE SEGMENTATION
FUSION
REGISTRATION
 
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.
 
Publisher IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
 
Date 2014-12-28T14:25:49Z
2014-12-28T14:25:49Z
2014
 
Type Article
 
Identifier IEEE TRANSACTIONS ON MEDICAL IMAGING, 33(9)1803-1817
0278-0062
1558-254X
http://dx.doi.org/10.1109/TMI.2014.2321281
http://dspace.library.iitb.ac.in/jspui/handle/100/16748
 
Language English