  Analysis of microscopy images can provide insight into many biological
processes. One particularly challenging problem is cell nuclear segmentation in
highly anisotropic and noisy 3D image data. Manually localizing and segmenting
each and every cell nuclei is very time consuming, which remains a bottleneck
in large scale biological experiments. In this work we present a tool for
automated segmentation of cell nuclei from 3D fluorescent microscopic data. Our
tool is based on state-of-the-art image processing and machine learning
techniques and supports a friendly graphical user interface (GUI). We show that
our tool is as accurate as manual annotation but greatly reduces the time for
the registration.
