Learning of Representative Multi-Resolution Multi-Object Statistical Shape Models from Small Training Populations

Matthias Wilms; Heinz Handels; Jan Ehrhardt

doi:10.1007/978-3-662-54345-0_81

Learning of Representative Multi-Resolution Multi-Object Statistical Shape Models from Small Training Populations

Matthias Wilms, Heinz Handels, Jan Ehrhardt

Institute of Medical Informatics

Abstract

Statistical shape models learned from a population of training shapes are frequently used as a shape prior. A key problem associated with their training is to provide a representative and large training set of (manual) segmentations. Therefore, models often suffer from the high-dimension-low-sample-size (HDLSS) problem, which limits their expressiveness and directly affects their performance.

Original language	English
Title of host publication	Bildverarbeitung für die Medizin 2017
Editors	K.H. Maier-Hein, T.M. Deserno, H. Handels, T. Tolxdorff
Number of pages	2
Publisher	Springer Vieweg, Berlin Heidelberg
Publication date	01.03.2017
Pages	359-360
ISBN (Print)	978-3-662-54344-3
ISBN (Electronic)	978-3-662-54345-0
DOIs	https://doi.org/10.1007/978-3-662-54345-0_81
Publication status	Published - 01.03.2017
Event	Bildverarbeitung für die Medizin 2017 - Heidelberg, Germany Duration: 12.03.2017 → 14.03.2017

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure

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10.1007/978-3-662-54345-0_81

https://www.springer.com/de/book/9783662543443

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Wilms, M., Handels, H., & Ehrhardt, J. (2017). Learning of Representative Multi-Resolution Multi-Object Statistical Shape Models from Small Training Populations. In K. H. Maier-Hein, T. M. Deserno, H. Handels, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2017 (pp. 359-360). (Informatik aktuell). Springer Vieweg, Berlin Heidelberg. https://doi.org/10.1007/978-3-662-54345-0_81

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title = "Learning of Representative Multi-Resolution Multi-Object Statistical Shape Models from Small Training Populations",

abstract = "Statistical shape models learned from a population of training shapes are frequently used as a shape prior. A key problem associated with their training is to provide a representative and large training set of (manual) segmentations. Therefore, models often suffer from the high-dimension-low-sample-size (HDLSS) problem, which limits their expressiveness and directly affects their performance.",

author = "Matthias Wilms and Heinz Handels and Jan Ehrhardt",

year = "2017",

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doi = "10.1007/978-3-662-54345-0\_81",

language = "English",

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booktitle = "Bildverarbeitung f{\"u}r die Medizin 2017",

note = "Bildverarbeitung f{\"u}r die Medizin 2017<br/> ; Conference date: 12-03-2017 Through 14-03-2017",

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Wilms, M, Handels, H & Ehrhardt, J 2017, Learning of Representative Multi-Resolution Multi-Object Statistical Shape Models from Small Training Populations. in KH Maier-Hein, TM Deserno, H Handels & T Tolxdorff (eds), Bildverarbeitung für die Medizin 2017. Informatik aktuell, Springer Vieweg, Berlin Heidelberg, pp. 359-360, Bildverarbeitung für die Medizin 2017
, Heidelberg, Germany, 12.03.17. https://doi.org/10.1007/978-3-662-54345-0_81

Learning of Representative Multi-Resolution Multi-Object Statistical Shape Models from Small Training Populations. / Wilms, Matthias; Handels, Heinz ; Ehrhardt, Jan.
Bildverarbeitung für die Medizin 2017. ed. / K.H. Maier-Hein; T.M. Deserno; H. Handels; T. Tolxdorff. Springer Vieweg, Berlin Heidelberg, 2017. p. 359-360 (Informatik aktuell).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

TY - GEN

T1 - Learning of Representative Multi-Resolution Multi-Object Statistical Shape Models from Small Training Populations

AU - Wilms, Matthias

AU - Handels, Heinz

AU - Ehrhardt, Jan

PY - 2017/3/1

Y1 - 2017/3/1

N2 - Statistical shape models learned from a population of training shapes are frequently used as a shape prior. A key problem associated with their training is to provide a representative and large training set of (manual) segmentations. Therefore, models often suffer from the high-dimension-low-sample-size (HDLSS) problem, which limits their expressiveness and directly affects their performance.

AB - Statistical shape models learned from a population of training shapes are frequently used as a shape prior. A key problem associated with their training is to provide a representative and large training set of (manual) segmentations. Therefore, models often suffer from the high-dimension-low-sample-size (HDLSS) problem, which limits their expressiveness and directly affects their performance.

U2 - 10.1007/978-3-662-54345-0_81

DO - 10.1007/978-3-662-54345-0_81

M3 - Conference contribution

SN - 978-3-662-54344-3

T3 - Informatik aktuell

SP - 359

EP - 360

BT - Bildverarbeitung für die Medizin 2017

A2 - Maier-Hein, K.H.

A2 - Deserno, T.M.

A2 - Handels, H.

A2 - Tolxdorff, T.

PB - Springer Vieweg, Berlin Heidelberg

T2 - Bildverarbeitung für die Medizin 2017<br/>

Y2 - 12 March 2017 through 14 March 2017

ER -

Learning of Representative Multi-Resolution Multi-Object Statistical Shape Models from Small Training Populations

Abstract

UN SDGs

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