A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.
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.
Department of Biostatistics, The Johns Hopkins University, Baltimore, Maryland, United States of America; Translational Neuroradiology Unit, Neuroimmunology Branch, National Institute of Neurological Disease and Stroke, National Institute of Health, Bethesda, Maryland, United States of America.
Elizabeth M
EM
Sweeney
Department of Statistical Science, Duke University, Durham, North Carolina, United States of America; Center for the Developing Brain, Child Mind Institute, New York, New York, United States of America.
Joshua T
JT
Vogelstein
Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Jennifer L
JL
Cuzzocreo
Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Peter A
PA
Calabresi
Department of Biostatistics, The Johns Hopkins University, Baltimore, Maryland, United States of America; Translational Neuroradiology Unit, Neuroimmunology Branch, National Institute of Neurological Disease and Stroke, National Institute of Health, Bethesda, Maryland, United States of America; Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America; Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Daniel S
DS
Reich
Department of Biostatistics, The Johns Hopkins University, Baltimore, Maryland, United States of America.
Ciprian M
CM
Crainiceanu
Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Russell T
RT
Shinohara
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000473:pathology*