Standard

Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia. / Venkatraghavan, Vikram; Dubost, Florian; Bron, Esther E.; Niessen, Wiro J.; de Bruijne, Marleen; Klein, Stefan.

Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings. ed. / Albert C.S. Chung; James C. Gee; Paul A. Yushkevich; Siqi Bao. Springer Verlag, 2019. p. 169-180 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11492 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Venkatraghavan, V, Dubost, F, Bron, EE, Niessen, WJ, de Bruijne, M & Klein, S 2019, Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia. in ACS Chung, JC Gee, PA Yushkevich & S Bao (eds), Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11492 LNCS, Springer Verlag, pp. 169-180, 26th International Conference on Information Processing in Medical Imaging, IPMI 2019, Hong Kong, China, 2/06/19. https://doi.org/10.1007/978-3-030-20351-1_13

APA

Venkatraghavan, V., Dubost, F., Bron, E. E., Niessen, W. J., de Bruijne, M., & Klein, S. (2019). Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia. In A. C. S. Chung, J. C. Gee, P. A. Yushkevich, & S. Bao (Eds.), Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings (pp. 169-180). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11492 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-20351-1_13

Vancouver

Venkatraghavan V, Dubost F, Bron EE, Niessen WJ, de Bruijne M, Klein S. Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia. In Chung ACS, Gee JC, Yushkevich PA, Bao S, editors, Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings. Springer Verlag. 2019. p. 169-180. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-20351-1_13

Author

Venkatraghavan, Vikram ; Dubost, Florian ; Bron, Esther E. ; Niessen, Wiro J. ; de Bruijne, Marleen ; Klein, Stefan. / Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia. Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings. editor / Albert C.S. Chung ; James C. Gee ; Paul A. Yushkevich ; Siqi Bao. Springer Verlag, 2019. pp. 169-180 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{27bb14a8fafb4b6e8de982fdf798a5de,
title = "Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia",
abstract = "Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying high-resolution images, potentially limiting the accuracy of EBM. Therefore, we propose a novel method that exploits high-dimensional voxel-wise imaging biomarkers: n-dimensional discriminative EBM (nDEBM). nDEBM is based on an insight that mixture modeling, which is a key element of conventional EBMs, can be replaced by a more scalable semi-supervised support vector machine (SVM) approach. This SVM is used to estimate the degree of abnormality of each region which is then used to obtain subject-specific disease progression patterns. These patterns are in turn used for estimating the mean ordering by fitting a generalized Mallows model. In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers’ Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions. SImBioTE trains variational auto-encoders (VAE) in different brain regions independently to simulate images at varying stages of disease progression. We also validate nDEBM clinically using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). In both experiments, nDEBM using high-dimensional features gave better performance than state-of-the-art EBM methods using regional volume biomarkers. This suggests that nDEBM is a promising approach for disease progression modeling.",
author = "Vikram Venkatraghavan and Florian Dubost and Bron, {Esther E.} and Niessen, {Wiro J.} and {de Bruijne}, Marleen and Stefan Klein",
year = "2019",
doi = "10.1007/978-3-030-20351-1_13",
language = "English",
isbn = "978-303020350-4",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "169--180",
editor = "Chung, {Albert C.S.} and Gee, {James C.} and Yushkevich, {Paul A.} and Siqi Bao",
booktitle = "Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings",

}

RIS

TY - GEN

T1 - Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia

AU - Venkatraghavan, Vikram

AU - Dubost, Florian

AU - Bron, Esther E.

AU - Niessen, Wiro J.

AU - de Bruijne, Marleen

AU - Klein, Stefan

PY - 2019

Y1 - 2019

N2 - Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying high-resolution images, potentially limiting the accuracy of EBM. Therefore, we propose a novel method that exploits high-dimensional voxel-wise imaging biomarkers: n-dimensional discriminative EBM (nDEBM). nDEBM is based on an insight that mixture modeling, which is a key element of conventional EBMs, can be replaced by a more scalable semi-supervised support vector machine (SVM) approach. This SVM is used to estimate the degree of abnormality of each region which is then used to obtain subject-specific disease progression patterns. These patterns are in turn used for estimating the mean ordering by fitting a generalized Mallows model. In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers’ Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions. SImBioTE trains variational auto-encoders (VAE) in different brain regions independently to simulate images at varying stages of disease progression. We also validate nDEBM clinically using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). In both experiments, nDEBM using high-dimensional features gave better performance than state-of-the-art EBM methods using regional volume biomarkers. This suggests that nDEBM is a promising approach for disease progression modeling.

AB - Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying high-resolution images, potentially limiting the accuracy of EBM. Therefore, we propose a novel method that exploits high-dimensional voxel-wise imaging biomarkers: n-dimensional discriminative EBM (nDEBM). nDEBM is based on an insight that mixture modeling, which is a key element of conventional EBMs, can be replaced by a more scalable semi-supervised support vector machine (SVM) approach. This SVM is used to estimate the degree of abnormality of each region which is then used to obtain subject-specific disease progression patterns. These patterns are in turn used for estimating the mean ordering by fitting a generalized Mallows model. In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers’ Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions. SImBioTE trains variational auto-encoders (VAE) in different brain regions independently to simulate images at varying stages of disease progression. We also validate nDEBM clinically using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). In both experiments, nDEBM using high-dimensional features gave better performance than state-of-the-art EBM methods using regional volume biomarkers. This suggests that nDEBM is a promising approach for disease progression modeling.

UR - http://www.scopus.com/inward/record.url?scp=85066145934&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-20351-1_13

DO - 10.1007/978-3-030-20351-1_13

M3 - Conference contribution

SN - 978-303020350-4

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

SP - 169

EP - 180

BT - Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings

A2 - Chung, Albert C.S.

A2 - Gee, James C.

A2 - Yushkevich, Paul A.

A2 - Bao, Siqi

PB - Springer Verlag

ER -

ID: 54238632