  Increasing data set sizes of 3D microscopy imaging experiments demand for an
automation of segmentation processes to be able to extract meaningful
biomedical information. Due to the shortage of annotated 3D image data that can
be used for machine learning-based approaches, 3D segmentation approaches are
required to be robust and to generalize well to unseen data. The Cellpose
approach proposed by Stringer et al. proved to be such a generalist approach
for cell instance segmentation tasks. In this paper, we extend the Cellpose
approach to improve segmentation accuracy on 3D image data and we further show
how the formulation of the gradient maps can be simplified while still being
robust and reaching similar segmentation accuracy. The code is publicly
available and was integrated into two established open-source applications that
allow using the 3D extension of Cellpose without any programming knowledge.
