Convolutional Neural Network Pruning to Accelerate Membrane Segmentation in Electron Microscopy

TL;DR

This paper introduces a CNN pruning method that reduces computational load and memory usage in membrane segmentation tasks, enabling real-time performance in electron microscopy analysis.

Contribution

The proposed pruning technique minimizes training loss increase and enhances efficiency without significantly compromising accuracy in membrane segmentation CNNs.

Findings

Pruned networks are more efficient in time and memory.

Retained accuracy after pruning and retraining.

Achieved real-time membrane segmentation performance.

Abstract

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.