  Data driven segmentation is an important initial step of shape prior-based
segmentation methods since it is assumed that the data term brings a curve to a
plausible level so that shape and data terms can then work together to produce
better segmentations. When purely data driven segmentation produces poor
results, the final segmentation is generally affected adversely. One challenge
faced by many existing data terms is due to the fact that they consider only
pixel intensities to decide whether to assign a pixel to the foreground or to
the background region. When the distributions of the foreground and background
pixel intensities have significant overlap, such data terms become ineffective,
as they produce uncertain results for many pixels in a test image. In such
cases, using prior information about the spatial context of the object to be
segmented together with the data term can bring a curve to a plausible stage,
which would then serve as a good initial point to launch shape-based
segmentation. In this paper, we propose a new segmentation approach that
combines nonparametric context priors with a learned-intensity-based data term
and nonparametric shape priors. We perform experiments for dendritic spine
segmentation in both 2D and 3D 2-photon microscopy images. The experimental
results demonstrate that using spatial context priors leads to significant
improvements.
