  Recent microscopy imaging techniques allow to precisely analyze cell
morphology in 3D image data. To process the vast amount of image data generated
by current digitized imaging techniques, automated approaches are demanded more
than ever. Segmentation approaches used for morphological analyses, however,
are often prone to produce unnaturally shaped predictions, which in conclusion
could lead to inaccurate experimental outcomes. In order to minimize further
manual interaction, shape priors help to constrain the predictions to the set
of natural variations. In this paper, we show how spherical harmonics can be
used as an alternative way to inherently constrain the predictions of neural
networks for the segmentation of cells in 3D microscopy image data. Benefits
and limitations of the spherical harmonic representation are analyzed and final
results are compared to other state-of-the-art approaches on two different data
sets.
