Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging

ENIGMA [Unknown], Dmitry Petrov, Boris A Gutman, Shih-Hua Julie Yu, Theo G M van Erp, Lianne Schmaal, Dick Veltman, Kathryn Alpert, Dmitry Isaev, Artemis Zavaliangos-Petropulu, Christopher R K Ching, Vince Calhoun, David Glahn, Theodore D Satterthwaite, Ole Andreas Andreasen, Stefan Borgwardt, Fleur Howells, Nynke Groenewold, Aristotle Voineskos, Joaquim Radua & 31 others Steven G Potkin, Benedicto Crespo-Facorro, Diana Tordesillas-Gutiérrez, Li Shen, Irina Lebedeva, Gianfranco Spalletta, Gary Donohoe, Peter Kochunov, Pedro G P Rosa, Anthony James, Udo Dannlowski, Bernhard T Baune, André Aleman, Ian H Gotlib, Henrik Walter, Martin Walter, Jair C Soares, Stefan Ehrlich, Ruben C Gur, N Trung Doan, Ingrid Agartz, Lars T Westlye, Fabienne Harrisberger, Anita Riecher-Rössler, Anne Uhlmann, Dan J Stein, Erin W Dickie, Edith Pomarol-Clotet, Paola Fuentes-Claramonte, Erick Jorge Canales-Rodríguez, Esther Walton

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

1 Citation (Scopus)

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 languageEnglish
Title of host publicationMachine Learning in Medical Imaging. MLMI 2017
EditorsQ. Wang, Y. Shi, H. I. Suk, K. Suzuki
Place of PublicationCham, Switzerland
PublisherSpringer
Pages371-378
Number of pages8
ISBN (Electronic)9783319673899
ISBN (Print)9783319673882
DOIs
Publication statusPublished - Sep 2017
Event8th International Workshop on Machine Learning in Medical Imaging (MLMI), 2017 - Quebec City, Canada
Duration: 10 Sep 201710 Sep 2017

Publication series

NameLecture Notes in Computer Science
Volume10541

Conference

Conference8th International Workshop on Machine Learning in Medical Imaging (MLMI), 2017
CountryCanada
CityQuebec City
Period10/09/1710/09/17

Cite this

[Unknown], ENIGMA., Petrov, D., Gutman, B. A., Yu, S-H. J., van Erp, T. G. M., Schmaal, L., ... Walton, E. (2017). Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging. In Q. Wang, Y. Shi, H. I. Suk, & K. Suzuki (Eds.), Machine Learning in Medical Imaging. MLMI 2017 (pp. 371-378). (Lecture Notes in Computer Science; Vol. 10541). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-67389-9_43

Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging. / [Unknown], ENIGMA; Petrov, Dmitry; Gutman, Boris A; Yu, Shih-Hua Julie; van Erp, Theo G M; Schmaal, Lianne; Veltman, Dick; Alpert, Kathryn; Isaev, Dmitry; Zavaliangos-Petropulu, Artemis; Ching, Christopher R K; Calhoun, Vince; Glahn, David; Satterthwaite, Theodore D; Andreasen, Ole Andreas; Borgwardt, Stefan; Howells, Fleur; Groenewold, Nynke; Voineskos, Aristotle; Radua, Joaquim; Potkin, Steven G; Crespo-Facorro, Benedicto; Tordesillas-Gutiérrez, Diana; Shen, Li; Lebedeva, Irina; Spalletta, Gianfranco; Donohoe, Gary; Kochunov, Peter; Rosa, Pedro G P; James, Anthony; Dannlowski, Udo; Baune, Bernhard T; Aleman, André; Gotlib, Ian H; Walter, Henrik; Walter, Martin; Soares, Jair C; Ehrlich, Stefan; Gur, Ruben C; Doan, N Trung; Agartz, Ingrid; Westlye, Lars T; Harrisberger, Fabienne; Riecher-Rössler, Anita; Uhlmann, Anne; Stein, Dan J; Dickie, Erin W; Pomarol-Clotet, Edith; Fuentes-Claramonte, Paola; Canales-Rodríguez, Erick Jorge; Walton, Esther.

Machine Learning in Medical Imaging. MLMI 2017. ed. / Q. Wang; Y. Shi; H. I. Suk; K. Suzuki. Cham, Switzerland : Springer, 2017. p. 371-378 (Lecture Notes in Computer Science; Vol. 10541).

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

[Unknown], ENIGMA, Petrov, D, Gutman, BA, Yu, S-HJ, van Erp, TGM, Schmaal, L, Veltman, D, Alpert, K, Isaev, D, Zavaliangos-Petropulu, A, Ching, CRK, Calhoun, V, Glahn, D, Satterthwaite, TD, Andreasen, OA, Borgwardt, S, Howells, F, Groenewold, N, Voineskos, A, Radua, J, Potkin, SG, Crespo-Facorro, B, Tordesillas-Gutiérrez, D, Shen, L, Lebedeva, I, Spalletta, G, Donohoe, G, Kochunov, P, Rosa, PGP, James, A, Dannlowski, U, Baune, BT, Aleman, A, Gotlib, IH, Walter, H, Walter, M, Soares, JC, Ehrlich, S, Gur, RC, Doan, NT, Agartz, I, Westlye, LT, Harrisberger, F, Riecher-Rössler, A, Uhlmann, A, Stein, DJ, Dickie, EW, Pomarol-Clotet, E, Fuentes-Claramonte, P, Canales-Rodríguez, EJ & Walton, E 2017, Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging. in Q Wang, Y Shi, HI Suk & K Suzuki (eds), Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science, vol. 10541, Springer, Cham, Switzerland, pp. 371-378, 8th International Workshop on Machine Learning in Medical Imaging (MLMI), 2017, Quebec City, Canada, 10/09/17. https://doi.org/10.1007/978-3-319-67389-9_43
[Unknown] ENIGMA, Petrov D, Gutman BA, Yu S-HJ, van Erp TGM, Schmaal L et al. Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging. In Wang Q, Shi Y, Suk HI, Suzuki K, editors, Machine Learning in Medical Imaging. MLMI 2017. Cham, Switzerland: Springer. 2017. p. 371-378. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-67389-9_43
[Unknown], ENIGMA ; Petrov, Dmitry ; Gutman, Boris A ; Yu, Shih-Hua Julie ; van Erp, Theo G M ; Schmaal, Lianne ; Veltman, Dick ; Alpert, Kathryn ; Isaev, Dmitry ; Zavaliangos-Petropulu, Artemis ; Ching, Christopher R K ; Calhoun, Vince ; Glahn, David ; Satterthwaite, Theodore D ; Andreasen, Ole Andreas ; Borgwardt, Stefan ; Howells, Fleur ; Groenewold, Nynke ; Voineskos, Aristotle ; Radua, Joaquim ; Potkin, Steven G ; Crespo-Facorro, Benedicto ; Tordesillas-Gutiérrez, Diana ; Shen, Li ; Lebedeva, Irina ; Spalletta, Gianfranco ; Donohoe, Gary ; Kochunov, Peter ; Rosa, Pedro G P ; James, Anthony ; Dannlowski, Udo ; Baune, Bernhard T ; Aleman, André ; Gotlib, Ian H ; Walter, Henrik ; Walter, Martin ; Soares, Jair C ; Ehrlich, Stefan ; Gur, Ruben C ; Doan, N Trung ; Agartz, Ingrid ; Westlye, Lars T ; Harrisberger, Fabienne ; Riecher-Rössler, Anita ; Uhlmann, Anne ; Stein, Dan J ; Dickie, Erin W ; Pomarol-Clotet, Edith ; Fuentes-Claramonte, Paola ; Canales-Rodríguez, Erick Jorge ; Walton, Esther. / Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging. Machine Learning in Medical Imaging. MLMI 2017. editor / Q. Wang ; Y. Shi ; H. I. Suk ; K. Suzuki. Cham, Switzerland : Springer, 2017. pp. 371-378 (Lecture Notes in Computer Science).
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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.",
author = "ENIGMA [Unknown] and Dmitry Petrov and Gutman, {Boris A} and Yu, {Shih-Hua Julie} and {van Erp}, {Theo G M} and Lianne Schmaal and Dick Veltman and Kathryn Alpert and Dmitry Isaev and Artemis Zavaliangos-Petropulu and Ching, {Christopher R K} and Vince Calhoun and David Glahn and Satterthwaite, {Theodore D} and Andreasen, {Ole Andreas} and Stefan Borgwardt and Fleur Howells and Nynke Groenewold and Aristotle Voineskos and Joaquim Radua and Potkin, {Steven G} and Benedicto Crespo-Facorro and Diana Tordesillas-Guti{\'e}rrez and Li Shen and Irina Lebedeva and Gianfranco Spalletta and Gary Donohoe and Peter Kochunov and Rosa, {Pedro G P} and Anthony James and Udo Dannlowski and Baune, {Bernhard T} and Andr{\'e} Aleman and Gotlib, {Ian H} and Henrik Walter and Martin Walter and Soares, {Jair C} and Stefan Ehrlich and Gur, {Ruben C} and Doan, {N Trung} and Ingrid Agartz and Westlye, {Lars T} and Fabienne Harrisberger and Anita Riecher-R{\"o}ssler and Anne Uhlmann and Stein, {Dan J} and Dickie, {Erin W} and Edith Pomarol-Clotet and Paola Fuentes-Claramonte and Canales-Rodr{\'i}guez, {Erick Jorge} and Esther Walton",
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AU - [Unknown], ENIGMA

AU - Petrov, Dmitry

AU - Gutman, Boris A

AU - Yu, Shih-Hua Julie

AU - van Erp, Theo G M

AU - Schmaal, Lianne

AU - Veltman, Dick

AU - Alpert, Kathryn

AU - Isaev, Dmitry

AU - Zavaliangos-Petropulu, Artemis

AU - Ching, Christopher R K

AU - Calhoun, Vince

AU - Glahn, David

AU - Satterthwaite, Theodore D

AU - Andreasen, Ole Andreas

AU - Borgwardt, Stefan

AU - Howells, Fleur

AU - Groenewold, Nynke

AU - Voineskos, Aristotle

AU - Radua, Joaquim

AU - Potkin, Steven G

AU - Crespo-Facorro, Benedicto

AU - Tordesillas-Gutiérrez, Diana

AU - Shen, Li

AU - Lebedeva, Irina

AU - Spalletta, Gianfranco

AU - Donohoe, Gary

AU - Kochunov, Peter

AU - Rosa, Pedro G P

AU - James, Anthony

AU - Dannlowski, Udo

AU - Baune, Bernhard T

AU - Aleman, André

AU - Gotlib, Ian H

AU - Walter, Henrik

AU - Walter, Martin

AU - Soares, Jair C

AU - Ehrlich, Stefan

AU - Gur, Ruben C

AU - Doan, N Trung

AU - Agartz, Ingrid

AU - Westlye, Lars T

AU - Harrisberger, Fabienne

AU - Riecher-Rössler, Anita

AU - Uhlmann, Anne

AU - Stein, Dan J

AU - Dickie, Erin W

AU - Pomarol-Clotet, Edith

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AU - Canales-Rodríguez, Erick Jorge

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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.

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

M3 - Conference contribution

SN - 9783319673882

T3 - Lecture Notes in Computer Science

SP - 371

EP - 378

BT - Machine Learning in Medical Imaging. MLMI 2017

A2 - Wang, Q.

A2 - Shi, Y.

A2 - Suk, H. I.

A2 - Suzuki, K.

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CY - Cham, Switzerland

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