Hot-Distance: Combining One-Hot and Signed Distance Embeddings for Segmentation
This addresses data scarcity in segmentation for microscopy imaging, but appears incremental as it builds on existing embedding methods.
The paper tackles the problem of limited usable training data for segmentation of subcellular structures in FIB-SEM by introducing Hot-Distance, a novel segmentation target that combines one-hot and signed distance embeddings, resulting in increased data utilization.
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).