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.
Highlights
Machine learning is a popular perspective for mining and analyzing large collections of medical data [1,2,3]
We focus on the extent to which the choice of machine learning or classification algorithm and the feature extraction function impact performance in one problem from medical research – supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI)
We examine lesion segmentation methods that are the composition of a classification algorithm and a feature extraction function
Summary
Machine learning is a popular perspective for mining and analyzing large collections of medical data [1,2,3]. We focus on the extent to which the choice of machine learning or classification algorithm and the feature extraction function impact performance in one problem from medical research – supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). The evaluation of the classification algorithms employed in supervised lesion segmentation methods is a function of classification accuracy. Computational efficiency and interpretability may be valued at the cost of classification accuracy. Our evaluation includes the computational time and resources required by each algorithm and the interpretability of the results produced by the algorithm. Comparison of machine learning techniques has been performed in other applications[4,5,6], but not to our knowledge in multiple sclerosis lesion segmentation. Many of the currently available comparisons do not consider computational time
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