  Despite its broad availability, volumetric information acquisition from
Bright-Field Microscopy (BFM) is inherently difficult due to the projective
nature of the acquisition process. We investigate the prediction of 3D cell
instances from a set of BFM Z-Stack images. We propose a novel two-stage weakly
supervised method for volumetric instance segmentation of cells which only
requires approximate cell centroids annotation. Created pseudo-labels are
thereby refined with a novel refinement loss with Z-stack guidance. The
evaluations show that our approach can generalize not only to BFM Z-Stack data,
but to other 3D cell imaging modalities. A comparison of our pipeline against
fully supervised methods indicates that the significant gain in reduced data
collection and labelling results in minor performance difference.
