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Estimating A Reference Standard Segmentation with Spatially Varying Performance Parameters: Local MAP STAPLE
Commowick O., Akhondi-Asl A., Warfield S. K.
IEEE Transactions on Medical Imaging 31, 8 (2012) 1593-1606 - http://www.hal.inserm.fr/inserm-00697775
 (22562727) 
Estimating A Reference Standard Segmentation with Spatially Varying Performance Parameters: Local MAP STAPLE
Olivier Commowick () 1, 2, Alireza Akhondi-Asl2, Simon Warfield2
1 :  VISAGES - VISAGES : Vision Action et Gestion d'Informations en Santé
http://www.inria.fr/equipes/visages
INSERM : U746 – CNRS : UMR6074 – INRIA – Université de Rennes 1
IRISA, campus de Beaulieu F-35042 Rennes
France
2 :  CRL - Computational Radiology Laboratory [Boston]
http://crl.med.harvard.edu/
Brigham and Women's Hospital - Harvard Medical School – Children's Hospital
Computational Radiology Laboratory Department of Radiology, Wolbach 215 Children's Hospital 300 Longwood Avenue Boston MA 02115 USA
États-Unis
We present a new algorithm, called local MAP STAPLE, to estimate from a set of multi-label segmentations both a reference standard segmentation and spatially varying performance parameters. It is based on a sliding window technique to estimate the segmentation and the segmentation performance parameters for each input segmentation. In order to allow for optimal fusion from the small amount of data in each local region, and to account for the possibility of labels not being observed in a local region of some (or all) input segmentations, we introduce prior probabilities for the local performance parameters through a new Maximum A Posteriori formulation of STAPLE. Further, we propose an expression to compute confidence intervals in the estimated local performance parameters. We carried out several experiments with local MAP STAPLE to characterize its performance and value for local segmentation evaluation. First, with simulated segmentations with known reference standard segmentation and spatially varying performance, we show that local MAP STAPLE performs better than both STAPLE and majority voting. Then we present evaluations with data sets from clinical applications. These experiments demonstrate that spatial adaptivity in segmentation performance is an important property to capture. We compared the local MAP STAPLE segmentations to STAPLE, and to previously published fusion techniques and demonstrate the superiority of local MAP STAPLE over other state-ofthe- art algorithms.
Sciences du Vivant/Ingénierie biomédicale
Informatique/Imagerie médicale
Anglais
1558-254X

Articles dans des revues avec comité de lecture
10.1109/TMI.2012.2197406
IEEE Transactions on Medical Imaging (IEEE Trans Med Imaging)
Publisher Institute of Electrical and Electronics Engineers (IEEE)
ISSN 0278-0062 
internationale
01/08/2012
02/05/2012
31
8
1593-1606

STAPLE – segmentation – label fusion – reference standard – performance evaluation.
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