We introduce the LSDs as an auxiliary learning task for boundary prediction. In a large scale study, we show that when compared to affinity-based methods, LSDs improve neuron segmentations across specimen, resolution, and imaging techniques. When considering performance on neuropil, LSDs implemented in an auto-context architecture are competitive with the current state of the art and two orders of magnitude more efficient.
So why do the LSDs improve segmentations?
We hypothesize that using LSDs as an auxiliary learning task incentivizes the network to consider higher-level features. Since additional local structure has to be considered, predicting LSDs is likely a harder task than vanilla boundary detection. The network is forced to make use of more information in its receptive field than is required for boundary prediction alone. This forces the network to correlate boundary prediction to LSD prediction.
While Long range affinities also use auxiliary learning (the extra neighborhood steps), we found they do not perform as well across the investigated datasets. This could result from several factors including blind spots (missing neighborhood steps), masking during training, and the isotropy of the data.
See the paper for further details on auto-context, masking and metrics.