  Biological membranes are one of the most basic structures and regions of
interest in cell biology. In the study of membranes, segment extraction is a
well-known and difficult problem because of impeding noise, directional and
thickness variability, etc. Recent advances in electron microscopy membrane
segmentation are able to cope with such difficulties by training convolutional
neural networks. However, because of the massive amount of features that have
to be extracted while propagating forward, the practical usability diminishes,
even with state-of-the-art GPU's. A significant part of these network features
typically contains redundancy through correlation and sparsity. In this work,
we propose a pruning method for convolutional neural networks that ensures the
training loss increase is minimized. We show that the pruned networks, after
retraining, are more efficient in terms of time and memory, without
significantly affecting the network accuracy. This way, we manage to obtain
real-time membrane segmentation performance, for our specific electron
microscopy setup.
