A new statistical image analysis approach and its application to Hippocampal morphometry

ADNI

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

1 Citation (Scopus)

Abstract

In this work, we propose a novel and powerful image analysis framework for hippocampal morphometry in early mild cognitive impairment (EMCI), an early prodromal stage of Alzheimer’s disease (AD). We create a hippocampal surface atlas with subfield information, model each hippocampus using the SPHARM technique, and register it to the atlas to extract surface deformation signals. We propose a new alternative to standard random field theory (RFT) and permutation image analysis methods, Statistical Parametric Mapping (SPM) Distribution Analysis or SPM-DA, to perform statistical shape analysis and compare its performance with that of RFT methods on both simulated and real hippocampal surface data. The major strengths of our framework are twofold: (a) SPM-DA provides potentially more powerful algorithms than standard RFT methods for detecting weak signals, and (b) the framework embraces the important hippocampal subfield information for improved biological interpretation. We demonstrate the effectiveness of our method via an application to an AD cohort, where an SPM-DA method detects meaningful hippocampal shape differences in EMCI that are undetected by standard RFT methods.

Original languageEnglish (US)
Title of host publicationMedical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings
PublisherSpringer Verlag
Pages302-310
Number of pages9
Volume9805
ISBN (Print)9783319437743
DOIs
StatePublished - 2016
Event7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016 - Bern, Switzerland
Duration: Aug 24 2016Aug 26 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9805
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016
CountrySwitzerland
CityBern
Period8/24/168/26/16

Fingerprint

Morphometry
Image Analysis
Image analysis
Statistical Analysis
Random Field
Field Theory
Alzheimer's Disease
Atlas
Subfield
Hippocampus
Shape Analysis
Statistical methods
Statistical method
Permutation
Alternatives
Demonstrate
Framework
Standards

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

ADNI (2016). A new statistical image analysis approach and its application to Hippocampal morphometry. In Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings (Vol. 9805, pp. 302-310). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9805). Springer Verlag. https://doi.org/10.1007/978-3-319-43775-0_27

A new statistical image analysis approach and its application to Hippocampal morphometry. / ADNI.

Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings. Vol. 9805 Springer Verlag, 2016. p. 302-310 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9805).

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

ADNI 2016, A new statistical image analysis approach and its application to Hippocampal morphometry. in Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings. vol. 9805, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9805, Springer Verlag, pp. 302-310, 7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016, Bern, Switzerland, 8/24/16. https://doi.org/10.1007/978-3-319-43775-0_27
ADNI. A new statistical image analysis approach and its application to Hippocampal morphometry. In Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings. Vol. 9805. Springer Verlag. 2016. p. 302-310. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-43775-0_27
ADNI. / A new statistical image analysis approach and its application to Hippocampal morphometry. Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings. Vol. 9805 Springer Verlag, 2016. pp. 302-310 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{74571f80e19f420f8a8c42211fadd15f,
title = "A new statistical image analysis approach and its application to Hippocampal morphometry",
abstract = "In this work, we propose a novel and powerful image analysis framework for hippocampal morphometry in early mild cognitive impairment (EMCI), an early prodromal stage of Alzheimer’s disease (AD). We create a hippocampal surface atlas with subfield information, model each hippocampus using the SPHARM technique, and register it to the atlas to extract surface deformation signals. We propose a new alternative to standard random field theory (RFT) and permutation image analysis methods, Statistical Parametric Mapping (SPM) Distribution Analysis or SPM-DA, to perform statistical shape analysis and compare its performance with that of RFT methods on both simulated and real hippocampal surface data. The major strengths of our framework are twofold: (a) SPM-DA provides potentially more powerful algorithms than standard RFT methods for detecting weak signals, and (b) the framework embraces the important hippocampal subfield information for improved biological interpretation. We demonstrate the effectiveness of our method via an application to an AD cohort, where an SPM-DA method detects meaningful hippocampal shape differences in EMCI that are undetected by standard RFT methods.",
author = "ADNI and Mark Inlow and Shan Cong and Risacher, {Shannon L.} and John West and Maher Rizkalla and Paul Salama and Andrew Saykin and Li Shen",
year = "2016",
doi = "10.1007/978-3-319-43775-0_27",
language = "English (US)",
isbn = "9783319437743",
volume = "9805",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "302--310",
booktitle = "Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings",

}

TY - GEN

T1 - A new statistical image analysis approach and its application to Hippocampal morphometry

AU - ADNI

AU - Inlow, Mark

AU - Cong, Shan

AU - Risacher, Shannon L.

AU - West, John

AU - Rizkalla, Maher

AU - Salama, Paul

AU - Saykin, Andrew

AU - Shen, Li

PY - 2016

Y1 - 2016

N2 - In this work, we propose a novel and powerful image analysis framework for hippocampal morphometry in early mild cognitive impairment (EMCI), an early prodromal stage of Alzheimer’s disease (AD). We create a hippocampal surface atlas with subfield information, model each hippocampus using the SPHARM technique, and register it to the atlas to extract surface deformation signals. We propose a new alternative to standard random field theory (RFT) and permutation image analysis methods, Statistical Parametric Mapping (SPM) Distribution Analysis or SPM-DA, to perform statistical shape analysis and compare its performance with that of RFT methods on both simulated and real hippocampal surface data. The major strengths of our framework are twofold: (a) SPM-DA provides potentially more powerful algorithms than standard RFT methods for detecting weak signals, and (b) the framework embraces the important hippocampal subfield information for improved biological interpretation. We demonstrate the effectiveness of our method via an application to an AD cohort, where an SPM-DA method detects meaningful hippocampal shape differences in EMCI that are undetected by standard RFT methods.

AB - In this work, we propose a novel and powerful image analysis framework for hippocampal morphometry in early mild cognitive impairment (EMCI), an early prodromal stage of Alzheimer’s disease (AD). We create a hippocampal surface atlas with subfield information, model each hippocampus using the SPHARM technique, and register it to the atlas to extract surface deformation signals. We propose a new alternative to standard random field theory (RFT) and permutation image analysis methods, Statistical Parametric Mapping (SPM) Distribution Analysis or SPM-DA, to perform statistical shape analysis and compare its performance with that of RFT methods on both simulated and real hippocampal surface data. The major strengths of our framework are twofold: (a) SPM-DA provides potentially more powerful algorithms than standard RFT methods for detecting weak signals, and (b) the framework embraces the important hippocampal subfield information for improved biological interpretation. We demonstrate the effectiveness of our method via an application to an AD cohort, where an SPM-DA method detects meaningful hippocampal shape differences in EMCI that are undetected by standard RFT methods.

UR - http://www.scopus.com/inward/record.url?scp=84984838237&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84984838237&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-43775-0_27

DO - 10.1007/978-3-319-43775-0_27

M3 - Conference contribution

AN - SCOPUS:84984838237

SN - 9783319437743

VL - 9805

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 302

EP - 310

BT - Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings

PB - Springer Verlag

ER -