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Analysis of microscopy images can provide insight into many biologicalprocesses. One particularly challenging problem is cell nuclear segmentation inhighly anisotropic and noisy 3D image data. Manually localizing and segmentingeach and every cell nuclei is very time consuming, which remains a bottleneckin large scale biological experiments. In this work we present a tool forautomated segmentation of cell nuclei from 3D fluorescent microscopic data. Ourtool is based on state-of-the-art image processing and machine learningtechniques and supports a friendly graphical user interface (GUI). We show thatour tool is as accurate as manual annotation but greatly reduces the time forthe registration.

GRED: Graph-Regularized 3D Shape Reconstruction from Highly Anisotropic and Noisy Images