Two-Stage Generative Models of Simulating Training Data at The Voxel Level for
Large-Scale Microscopy Bioimage Segmentation
Abstract
Bioimage Informatics is a growing area that aims to
extract biological knowledge from microscope images of biomedical samples automatically. Its mission is vastly challenging, however, due to the complexity of diverse imaging modalities and big scales
of multi-dimensional images. One major challenge
is automatic image segmentation, an essential step
towards high-level modeling and analysis. While
progresses in deep learning have brought the goal
of automation much closer to reality, creating training data for producing powerful neural networks
is often laborious. To provide a shortcut for this
costly step, we propose a novel two-stage generative model for simulating voxel level training data
based on a specially designed objective function
of preserving foreground labels. Using segmenting
neurons from LM (Light Microscopy) image stacks
as a testing example, we showed that segmentation
networks trained by our synthetic data were able to
produce satisfactory results. Unlike other simulation methods available in the field, our method can
be easily extended to many other applications because it does not involve sophisticated cell models
and imaging mechanisms