  Fluorescence microscopy is an essential tool for the analysis of 3D
subcellular structures in tissue. An important step in the characterization of
tissue involves nuclei segmentation. In this paper, a two-stage method for
segmentation of nuclei using convolutional neural networks (CNNs) is described.
In particular, since creating labeled volumes manually for training purposes is
not practical due to the size and complexity of the 3D data sets, the paper
describes a method for generating synthetic microscopy volumes based on a
spatially constrained cycle-consistent adversarial network. The proposed method
is tested on multiple real microscopy data sets and outperforms other commonly
used segmentation techniques.
