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.
Cite
@article{arxiv.1707.06353,
title = {Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging},
author = {Dmitry Petrov and Boris A. Gutman and Shih-Hua and Yu and Theo G. M. van Erp and Jessica A. Turner and Lianne Schmaal and Dick Veltman and Lei Wang and Kathryn Alpert and Dmitry Isaev and Artemis Zavaliangos-Petropulu and Christopher R. K. Ching and Vince Calhoun and David Glahn and Theodore D. Satterthwaite and Ole Andreas Andreasen and Stefan Borgwardt and Fleur Howells and Nynke Groenewold and Aristotle Voineskos and Joaquim Radua and Steven G. Potkin and Benedicto Crespo-Facorro and Diana Tordesillas-Gutierrez and Li Shen and Irina Lebedeva and Gianfranco Spalletta and Gary Donohoe and Peter Kochunov and Pedro G. P. Rosa and Anthony James and Udo Dannlowski and Bernhard T. Baune and Andre Aleman and Ian H. Gotlib and Henrik Walter and Martin Walter and Jair C. Soares and Stefan Ehrlich and Ruben C. Gur and N. Trung Doan and Ingrid Agartz and Lars T. Westlye and Fabienne Harrisberger and Anita Riecher-Rossler and Anne Uhlmann and Dan J. Stein and Erin W. Dickie and Edith Pomarol-Clotet and Paola Fuentes-Claramonte and Erick Jorge Canales-Rodriguez and Raymond Salvador and Alexander J. Huang and Roberto Roiz-Santianez and Shan Cong and Alexander Tomyshev and Fabrizio Piras and Daniela Vecchio and Nerisa Banaj and Valentina Ciullo and Elliot Hong and Geraldo Busatto and Marcus V. Zanetti and Mauricio H. Serpa and Simon Cervenka and Sinead Kelly and Dominik Grotegerd and Matthew D. Sacchet and Ilya M. Veer and Meng Li and Mon-Ju Wu and Benson Irungu and Esther Walton and Paul M. Thompson},
journal= {arXiv preprint arXiv:1707.06353},
year = {2017}
}
Comments
Arxiv version of the MICCAI 2017 Machine Learning in Medical Imaging workshop paper