  Purpose: Lung nodule segmentation, i.e., the algorithmic delineation of the
lung nodule surface, is a fundamental component of computational nodule
analysis pipelines. We propose a new method for segmentation that is a machine
learning based extension of current approaches, using labeled image examples to
improve its accuracy.
  Approach: We introduce an extension of the standard level set image
segmentation method where the velocity function is learned from data via
machine learning regression methods, rather than a priori designed. Instead,
the method employs a set of features to learn a velocity function that guides
the level set evolution from initialization.
  Results: We apply the method to image volumes of lung nodules from CT scans
in the publicly available LIDC dataset, obtaining an average intersection over
union score of 0.7185($\pm$0.1114), which is competitive with other methods. We
analyze segmentation performance by anatomical and appearance-based categories
of the nodules, finding that the method performs better for isolated nodules
with well-defined margins. We find that the segmentation performance for
nodules in more complex surroundings and having more complex CT appearance is
improved with the addition of combined global-local features.
  Conclusions: The level set machine learning segmentation approach proposed
herein is competitive with current methods. It provides accurate lung nodule
segmentation results in a variety of anatomical contexts.
