Prostate shape modeling based on principal geodesic analysis bootstrapping

Erik Dam; P. Thomas Fletcher; Stephen M. Pizer; Gregg Tracton; Julian Rosenman

doi:10.1007/978-3-540-30136-3_122

Prostate shape modeling based on principal geodesic analysis bootstrapping

Erik Dam
, P. Thomas Fletcher
, Stephen M. Pizer
, Gregg Tracton
, Julian Rosenman

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

The use of statistical shape models in medical image analysis is growing due to the ability to incorporate prior organ shape knowledge for tasks such as segmentation, registration, and classification. Shape models are trained from collections of segmented organs. Though manual interaction during training can ensure correspondence, it also introduces bias and ruins reproducibility - automation is desirable. We present a novel shape model construction method via a medial shape representation. The automatic method is based on an iterative bootstrap method that alternates between shape representation optimization and analysis of shape mean and variations. The method is used to create a model from 46 segmented prostates with quantitatively and intutitively good results.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2004
Number of pages	9
Publisher	Springer
Publication date	2004
Pages	1008-1016
DOIs	https://doi.org/10.1007/978-3-540-30136-3_122
Publication status	Published - 2004
Externally published	Yes
Event	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings - Saint-Malo, France Duration: 26 Sept 2004 → 29 Sept 2004

Conference

Conference	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings
Country/Territory	France
City	Saint-Malo
Period	26/09/2004 → 29/09/2004

Series	Lecture Notes in Computer Science
Volume	3217
ISSN	0302-9743

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10.1007/978-3-540-30136-3_122

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Prostate shape modeling based on principal geodesic analysis bootstrapping. / Dam, Erik; Fletcher, P. Thomas; Pizer, Stephen M. et al.
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2004. Springer, 2004. p. 1008-1016 (Lecture Notes in Computer Science, Vol. 3217).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Dam, E, Fletcher, PT, Pizer, SM, Tracton, G & Rosenman, J 2004, Prostate shape modeling based on principal geodesic analysis bootstrapping. in Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2004. Springer, Lecture Notes in Computer Science, vol. 3217, pp. 1008-1016, Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings, Saint-Malo, France, 26/09/2004. https://doi.org/10.1007/978-3-540-30136-3_122

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abstract = "The use of statistical shape models in medical image analysis is growing due to the ability to incorporate prior organ shape knowledge for tasks such as segmentation, registration, and classification. Shape models are trained from collections of segmented organs. Though manual interaction during training can ensure correspondence, it also introduces bias and ruins reproducibility - automation is desirable. We present a novel shape model construction method via a medial shape representation. The automatic method is based on an iterative bootstrap method that alternates between shape representation optimization and analysis of shape mean and variations. The method is used to create a model from 46 segmented prostates with quantitatively and intutitively good results.",

author = "Erik Dam and Fletcher, \{P. Thomas\} and Pizer, \{Stephen M.\} and Gregg Tracton and Julian Rosenman",

year = "2004",

doi = "10.1007/978-3-540-30136-3\_122",

language = "English",

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AU - Tracton, Gregg

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N2 - The use of statistical shape models in medical image analysis is growing due to the ability to incorporate prior organ shape knowledge for tasks such as segmentation, registration, and classification. Shape models are trained from collections of segmented organs. Though manual interaction during training can ensure correspondence, it also introduces bias and ruins reproducibility - automation is desirable. We present a novel shape model construction method via a medial shape representation. The automatic method is based on an iterative bootstrap method that alternates between shape representation optimization and analysis of shape mean and variations. The method is used to create a model from 46 segmented prostates with quantitatively and intutitively good results.

AB - The use of statistical shape models in medical image analysis is growing due to the ability to incorporate prior organ shape knowledge for tasks such as segmentation, registration, and classification. Shape models are trained from collections of segmented organs. Though manual interaction during training can ensure correspondence, it also introduces bias and ruins reproducibility - automation is desirable. We present a novel shape model construction method via a medial shape representation. The automatic method is based on an iterative bootstrap method that alternates between shape representation optimization and analysis of shape mean and variations. The method is used to create a model from 46 segmented prostates with quantitatively and intutitively good results.

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AN - SCOPUS:20344391367

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T2 - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings

Y2 - 26 September 2004 through 29 September 2004

ER -

Prostate shape modeling based on principal geodesic analysis bootstrapping

Abstract

Conference

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