Model-based segmentation of hippocampal subfields in ultra-high resolution in vivo MRI

Koen Van Leemput; Akram Bakkour; Thomas Benner; Graham Wiggins; Lawrence L. Wald; Jean Augustinack; Bradford C. Dickerson; Polina Golland; Bruce Fischl

doi:10.1007/978-3-540-85988-8_29

Model-based segmentation of hippocampal subfields in ultra-high resolution in vivo MRI

Koen Van Leemput
, Akram Bakkour
, Thomas Benner
, Graham Wiggins
, Lawrence L. Wald
, Jean Augustinack
, Bradford C. Dickerson
, Polina Golland
, Bruce Fischl

Computer Science and Artificial Intelligence Laboratory, MIT
Massachusetts General Hospital/Harvard Medical School

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

Abstract

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. In this paper, we propose a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution MRI data. Using a Bayesian approach, we build a computational model of how images around the hippocampal area are generated, and use this model to obtain automated segmentations. We validate the proposed technique by comparing our segmentation results with corresponding manual delineations in ultra-high resolution MRI scans of five individuals. © 2008 Springer-Verlag Berlin Heidelberg.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention
Number of pages	9
Volume	5241
Publisher	Springer-verlag Berlin
Publication date	2008
Pages	235-243
ISBN (Print)	978-3-540-85987-1
DOIs	https://doi.org/10.1007/978-3-540-85988-8_29
Publication status	Published - 2008
Externally published	Yes
Event	11th International Conference on Medical Image Computing and Computer Assisted Intervention - New York University, New York, NY, United States Duration: 6 Sept 2008 → 10 Sept 2008 Conference number: 11 http://miccai2008.rutgers.edu/

Conference

Conference	11th International Conference on Medical Image Computing and Computer Assisted Intervention
Number	11
Location	New York University
Country/Territory	United States
City	New York, NY
Period	06/09/2008 → 10/09/2008
Internet address	http://miccai2008.rutgers.edu/

Series	Lecture Notes in Computer Science
ISSN	0302-9743

Keywords

Bayesian networks
Computer science
Image segmentation
Security of data
Technology transfer
Medical computing

Access to Document

10.1007/978-3-540-85988-8_29

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Van Leemput, K., Bakkour, A., Benner, T., Wiggins, G., Wald, L. L., Augustinack, J., Dickerson, B. C., Golland, P., & Fischl, B. (2008). Model-based segmentation of hippocampal subfields in ultra-high resolution in vivo MRI. In Medical Image Computing and Computer-Assisted Intervention (Vol. 5241, pp. 235-243). Springer-verlag Berlin. https://doi.org/10.1007/978-3-540-85988-8_29

@inproceedings{28251b4377364aea94df259f4d4a2e34,

title = "Model-based segmentation of hippocampal subfields in ultra-high resolution in vivo MRI",

abstract = "Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. In this paper, we propose a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution MRI data. Using a Bayesian approach, we build a computational model of how images around the hippocampal area are generated, and use this model to obtain automated segmentations. We validate the proposed technique by comparing our segmentation results with corresponding manual delineations in ultra-high resolution MRI scans of five individuals. {\textcopyright} 2008 Springer-Verlag Berlin Heidelberg.",

keywords = "Bayesian networks, Computer science, Image segmentation, Security of data, Technology transfer, Medical computing",

author = "\{Van Leemput\}, Koen and Akram Bakkour and Thomas Benner and Graham Wiggins and Wald, \{Lawrence L.\} and Jean Augustinack and Dickerson, \{Bradford C.\} and Polina Golland and Bruce Fischl",

year = "2008",

doi = "10.1007/978-3-540-85988-8\_29",

language = "English",

isbn = "978-3-540-85987-1",

volume = "5241",

series = "Lecture Notes in Computer Science",

publisher = "Springer-verlag Berlin",

pages = "235--243",

booktitle = "Medical Image Computing and Computer-Assisted Intervention",

note = "11th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2008 ; Conference date: 06-09-2008 Through 10-09-2008",

url = "http://miccai2008.rutgers.edu/",

}

Van Leemput, K, Bakkour, A, Benner, T, Wiggins, G, Wald, LL, Augustinack, J, Dickerson, BC, Golland, P & Fischl, B 2008, Model-based segmentation of hippocampal subfields in ultra-high resolution in vivo MRI. in Medical Image Computing and Computer-Assisted Intervention. vol. 5241, Springer-verlag Berlin, Lecture Notes in Computer Science, pp. 235-243, 11th International Conference on Medical Image Computing and Computer Assisted Intervention, New York, NY, United States, 06/09/2008. https://doi.org/10.1007/978-3-540-85988-8_29

Model-based segmentation of hippocampal subfields in ultra-high resolution in vivo MRI. / Van Leemput, Koen; Bakkour, Akram; Benner, Thomas et al.
Medical Image Computing and Computer-Assisted Intervention. Vol. 5241 Springer-verlag Berlin, 2008. p. 235-243 (Lecture Notes in Computer Science).

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

TY - GEN

T1 - Model-based segmentation of hippocampal subfields in ultra-high resolution in vivo MRI

AU - Van Leemput, Koen

AU - Bakkour, Akram

AU - Benner, Thomas

AU - Wiggins, Graham

AU - Wald, Lawrence L.

AU - Augustinack, Jean

AU - Dickerson, Bradford C.

AU - Golland, Polina

AU - Fischl, Bruce

N1 - Conference code: 11

PY - 2008

Y1 - 2008

N2 - Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. In this paper, we propose a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution MRI data. Using a Bayesian approach, we build a computational model of how images around the hippocampal area are generated, and use this model to obtain automated segmentations. We validate the proposed technique by comparing our segmentation results with corresponding manual delineations in ultra-high resolution MRI scans of five individuals. © 2008 Springer-Verlag Berlin Heidelberg.

AB - Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. In this paper, we propose a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution MRI data. Using a Bayesian approach, we build a computational model of how images around the hippocampal area are generated, and use this model to obtain automated segmentations. We validate the proposed technique by comparing our segmentation results with corresponding manual delineations in ultra-high resolution MRI scans of five individuals. © 2008 Springer-Verlag Berlin Heidelberg.

KW - Bayesian networks

KW - Computer science

KW - Image segmentation

KW - Security of data

KW - Technology transfer

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VL - 5241

T3 - Lecture Notes in Computer Science

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EP - 243

BT - Medical Image Computing and Computer-Assisted Intervention

PB - Springer-verlag Berlin

T2 - 11th International Conference on Medical Image Computing and Computer Assisted Intervention

Y2 - 6 September 2008 through 10 September 2008

ER -

Model-based segmentation of hippocampal subfields in ultra-high resolution in vivo MRI

Abstract

Conference

Keywords

Access to Document

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Cite this