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A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

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Listed:
  • Elizabeth M Sweeney
  • Joshua T Vogelstein
  • Jennifer L Cuzzocreo
  • Peter A Calabresi
  • Daniel S Reich
  • Ciprian M Crainiceanu
  • Russell T Shinohara

Abstract

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

Suggested Citation

  • Elizabeth M Sweeney & Joshua T Vogelstein & Jennifer L Cuzzocreo & Peter A Calabresi & Daniel S Reich & Ciprian M Crainiceanu & Russell T Shinohara, 2014. "A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-14, April.
  • Handle: RePEc:plo:pone00:0095753
    DOI: 10.1371/journal.pone.0095753
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    References listed on IDEAS

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    1. Bordier, Cécile & Dojat, Michel & Micheaux, Pierre Lafaye de, 2011. "Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 44(i09).
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    1. Fahimeh Hadavimoghaddam & Mehdi Ostadhassan & Ehsan Heidaryan & Mohammad Ali Sadri & Inna Chapanova & Evgeny Popov & Alexey Cheremisin & Saeed Rafieepour, 2021. "Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations," Energies, MDPI, vol. 14(4), pages 1-16, February.
    2. Raymond Salvador & Joaquim Radua & Erick J Canales-Rodríguez & Aleix Solanes & Salvador Sarró & José M Goikolea & Alicia Valiente & Gemma C Monté & María del Carmen Natividad & Amalia Guerrero-Pedraza, 2017. "Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-24, April.

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