Morphological appearance manifolds in computational anatomy

Groupwise registration and morphological analysis

Christos Davatzikos, Naixiang Lian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present an extension of the conventional computational anatomy framework to account for confounding variations due to selection of parameters and templates, by learning the equivalence class derived from the multitude of representations of an individual anatomy. A morphological appearance manifold obtained by varying parameters of the template warping procedure is estimated. Group-wise registration and statistical analysis is then based on a constrained optimization framework, which employs a minimum variance criterion to perform manifold walking, i.e. to traverse each individual's morphological appearance manifold until group variance is minimal. Effectively, this process removes the aforementioned confounding effects and potentially leads to morphological representations that reflect purely underlying biological variations, instead of variations introduced by modeling assumptions and parameter settings. The nonlinearity of a morphological appearance manifold is treated via local linear approximations of the manifold via PCA.

Original languageEnglish (US)
Title of host publicationProceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
Pages826
Number of pages1
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 - Boston, MA, United States
Duration: Jun 28 2009Jul 1 2009

Other

Other2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
CountryUnited States
CityBoston, MA
Period6/28/097/1/09

Fingerprint

Equivalence classes
Constrained optimization
Anatomy
Statistical methods
Passive Cutaneous Anaphylaxis
Walking
Learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Davatzikos, C., & Lian, N. (2009). Morphological appearance manifolds in computational anatomy: Groupwise registration and morphological analysis. In Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 (pp. 826). [5193179] https://doi.org/10.1109/ISBI.2009.5193179

Morphological appearance manifolds in computational anatomy : Groupwise registration and morphological analysis. / Davatzikos, Christos; Lian, Naixiang.

Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. 2009. p. 826 5193179.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Davatzikos, C & Lian, N 2009, Morphological appearance manifolds in computational anatomy: Groupwise registration and morphological analysis. in Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009., 5193179, pp. 826, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, Boston, MA, United States, 6/28/09. https://doi.org/10.1109/ISBI.2009.5193179
Davatzikos C, Lian N. Morphological appearance manifolds in computational anatomy: Groupwise registration and morphological analysis. In Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. 2009. p. 826. 5193179 https://doi.org/10.1109/ISBI.2009.5193179
Davatzikos, Christos ; Lian, Naixiang. / Morphological appearance manifolds in computational anatomy : Groupwise registration and morphological analysis. Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. 2009. pp. 826
@inproceedings{472db5dd86da492e91e0175fce0deca6,
title = "Morphological appearance manifolds in computational anatomy: Groupwise registration and morphological analysis",
abstract = "We present an extension of the conventional computational anatomy framework to account for confounding variations due to selection of parameters and templates, by learning the equivalence class derived from the multitude of representations of an individual anatomy. A morphological appearance manifold obtained by varying parameters of the template warping procedure is estimated. Group-wise registration and statistical analysis is then based on a constrained optimization framework, which employs a minimum variance criterion to perform manifold walking, i.e. to traverse each individual's morphological appearance manifold until group variance is minimal. Effectively, this process removes the aforementioned confounding effects and potentially leads to morphological representations that reflect purely underlying biological variations, instead of variations introduced by modeling assumptions and parameter settings. The nonlinearity of a morphological appearance manifold is treated via local linear approximations of the manifold via PCA.",
author = "Christos Davatzikos and Naixiang Lian",
year = "2009",
doi = "10.1109/ISBI.2009.5193179",
language = "English (US)",
isbn = "9781424439324",
pages = "826",
booktitle = "Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009",

}

TY - GEN

T1 - Morphological appearance manifolds in computational anatomy

T2 - Groupwise registration and morphological analysis

AU - Davatzikos, Christos

AU - Lian, Naixiang

PY - 2009

Y1 - 2009

N2 - We present an extension of the conventional computational anatomy framework to account for confounding variations due to selection of parameters and templates, by learning the equivalence class derived from the multitude of representations of an individual anatomy. A morphological appearance manifold obtained by varying parameters of the template warping procedure is estimated. Group-wise registration and statistical analysis is then based on a constrained optimization framework, which employs a minimum variance criterion to perform manifold walking, i.e. to traverse each individual's morphological appearance manifold until group variance is minimal. Effectively, this process removes the aforementioned confounding effects and potentially leads to morphological representations that reflect purely underlying biological variations, instead of variations introduced by modeling assumptions and parameter settings. The nonlinearity of a morphological appearance manifold is treated via local linear approximations of the manifold via PCA.

AB - We present an extension of the conventional computational anatomy framework to account for confounding variations due to selection of parameters and templates, by learning the equivalence class derived from the multitude of representations of an individual anatomy. A morphological appearance manifold obtained by varying parameters of the template warping procedure is estimated. Group-wise registration and statistical analysis is then based on a constrained optimization framework, which employs a minimum variance criterion to perform manifold walking, i.e. to traverse each individual's morphological appearance manifold until group variance is minimal. Effectively, this process removes the aforementioned confounding effects and potentially leads to morphological representations that reflect purely underlying biological variations, instead of variations introduced by modeling assumptions and parameter settings. The nonlinearity of a morphological appearance manifold is treated via local linear approximations of the manifold via PCA.

UR - http://www.scopus.com/inward/record.url?scp=70449341663&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70449341663&partnerID=8YFLogxK

U2 - 10.1109/ISBI.2009.5193179

DO - 10.1109/ISBI.2009.5193179

M3 - Conference contribution

SN - 9781424439324

SP - 826

BT - Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

ER -