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

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

3 Scopus citations

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 (US)
Title of host publicationMachine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
EditorsYinghuan Shi, Heung-Il Suk, Kenji Suzuki, Qian Wang
PublisherSpringer Verlag
Pages371-378
Number of pages8
ISBN (Print)9783319673882
DOIs
StatePublished - 2017
Event8th 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: Sep 10 2017Sep 10 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10541 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th 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
CountryCanada
CityQuebec City
Period9/10/179/10/17

Keywords

  • Machine learning
  • Quality control
  • Shape analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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