Machine learning for large-scale quality control of 3D shape models in neuroimaging

Dmitry Petrov; Boris A. Gutman; Shih Hua Julie Yu; Kathryn Alpert; Artemis Zavaliangos-Petropulu; Dmitry Isaev; Jessica A. Turner; Theo G.M. van Erp; Lei Wang; Lianne Schmaal; Dick Veltman; Paul M. Thompson

doi:10.1007/978-3-319-67389-9_43

Machine learning for large-scale quality control of 3D shape models in neuroimaging

Dmitry Petrov
, Boris A. Gutman^*
, Shih Hua Julie Yu
, Kathryn Alpert
, Artemis Zavaliangos-Petropulu
, Dmitry Isaev
, Jessica A. Turner
, Theo G.M. van Erp
, Lei Wang
, Lianne Schmaal
, Dick Veltman
, Paul M. Thompson

^*Corresponding author for this work

University of Southern California
Russian Academy of Sciences
Northwestern University
Child Mind Institute
University of California at Irvine
ORYGEN Youth Health
VU Medical Center

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30–70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.

Original language	English
Title of host publication	Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
Publisher	Springer Verlag
Pages	371-378
Number of pages	8
ISBN (Print)	9783319673882
DOIs	https://doi.org/10.1007/978-3-319-67389-9_43
Publication status	Published - 2017
Event	8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: 10 Sept 2017 → 10 Sept 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10541 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Country/Territory	Canada
City	Quebec City
Period	10/09/2017 → 10/09/2017

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Machine learning
Quality control
Shape analysis

Access to Document

10.1007/978-3-319-67389-9_43

Machine-learning-for-large-scale-quality-control-of-3d-shape-models-in-neuroimaging.pdfFinal published version, 71.9 MBLicence: Taverne

Cite this

Petrov, D., Gutman, B. A., Yu, S. H. J., Alpert, K., Zavaliangos-Petropulu, A., Isaev, D., Turner, J. A., van Erp, T. G. M., Wang, L., Schmaal, L., Veltman, D., & Thompson, P. M. (2017). Machine learning for large-scale quality control of 3D shape models in neuroimaging. In Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings (pp. 371-378). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10541 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67389-9_43

Petrov, Dmitry ; Gutman, Boris A. ; Yu, Shih Hua Julie et al. / Machine learning for large-scale quality control of 3D shape models in neuroimaging. Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Springer Verlag, 2017. pp. 371-378 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{9e52c6ac058341389a0b920d99f5183a,

title = "Machine learning for large-scale quality control of 3D shape models in neuroimaging",

abstract = "As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30–70\%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.",

keywords = "Machine learning, Quality control, Shape analysis",

author = "Dmitry Petrov and Gutman, \{Boris A.\} and Yu, \{Shih Hua Julie\} and Kathryn Alpert and Artemis Zavaliangos-Petropulu and Dmitry Isaev and Turner, \{Jessica A.\} and \{van Erp\}, \{Theo G.M.\} and Lei Wang and Lianne Schmaal and Dick Veltman and Thompson, \{Paul M.\}",

note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 ; Conference date: 10-09-2017 Through 10-09-2017",

year = "2017",

doi = "10.1007/978-3-319-67389-9\_43",

language = "English",

isbn = "9783319673882",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "371--378",

booktitle = "Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings",

address = "Germany",

}

Petrov, D, Gutman, BA, Yu, SHJ, Alpert, K, Zavaliangos-Petropulu, A, Isaev, D, Turner, JA, van Erp, TGM, Wang, L, Schmaal, L, Veltman, D & Thompson, PM 2017, Machine learning for large-scale quality control of 3D shape models in neuroimaging. in Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10541 LNCS, Springer Verlag, pp. 371-378, 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, Canada, 10/09/2017. https://doi.org/10.1007/978-3-319-67389-9_43

Machine learning for large-scale quality control of 3D shape models in neuroimaging. / Petrov, Dmitry; Gutman, Boris A.; Yu, Shih Hua Julie et al.
Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Springer Verlag, 2017. p. 371-378 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10541 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Machine learning for large-scale quality control of 3D shape models in neuroimaging

AU - Petrov, Dmitry

AU - Gutman, Boris A.

AU - Yu, Shih Hua Julie

AU - Alpert, Kathryn

AU - Zavaliangos-Petropulu, Artemis

AU - Isaev, Dmitry

AU - Turner, Jessica A.

AU - van Erp, Theo G.M.

AU - Wang, Lei

AU - Schmaal, Lianne

AU - Veltman, Dick

AU - Thompson, Paul M.

PY - 2017

Y1 - 2017

N2 - As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30–70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.

AB - As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30–70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.

KW - Machine learning

KW - Quality control

KW - Shape analysis

UR - https://www.scopus.com/pages/publications/85029674745

U2 - 10.1007/978-3-319-67389-9_43

DO - 10.1007/978-3-319-67389-9_43

M3 - Conference contribution

SN - 9783319673882

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

SP - 371

EP - 378

BT - Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings

PB - Springer Verlag

T2 - 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017

Y2 - 10 September 2017 through 10 September 2017

ER -

Petrov D, Gutman BA, Yu SHJ, Alpert K, Zavaliangos-Petropulu A, Isaev D et al. Machine learning for large-scale quality control of 3D shape models in neuroimaging. In Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Springer Verlag. 2017. p. 371-378. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-67389-9_43

Machine learning for large-scale quality control of 3D shape models in neuroimaging

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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