  The Pancreatic beta cell is an important target in diabetes research. For
scalable modeling of beta cell ultrastructure, we investigate automatic
segmentation of whole cell imaging data acquired through soft X-ray tomography.
During the course of the study, both complete and partial ultrastructure
annotations were produced manually for different subsets of the data. To more
effectively use existing annotations, we propose a method that enables the
application of partially labeled data for full label segmentation. For
experimental validation, we apply our method to train a convolutional neural
network with a set of 12 fully annotated data and 12 partially annotated data
and show promising improvement over standard training that uses fully annotated
data alone.
