  Machine learning models are only as good as the data to which they are fit.
As such, it is always preferable to use as much data as possible in training
models. What data can be used for fitting a model depends a lot on the
formulation of the task. We introduce Hot-Distance, a novel segmentation target
that incorporates the strength of signed boundary distance prediction with the
flexibility of one-hot encoding, to increase the amount of usable training data
for segmentation of subcellular structures in focused ion beam scanning
electron microscopy (FIB-SEM).
