Artificial neural network filters for enhancing 3D optical microscopy images of neurites

Abstract

The ability to extract accurate morphology of labeled neurons from microscopy images is crucial for mapping brain connectivity and for understanding changes in connectivity that underlie learning and memory formation. There are, however, two problems, specific to optical microscopy imaging of neurons, which make accurate neuron tracing exceedingly challenging: (i) neurites can appear broken due to inhomogeneous labeling and (ii) neurites can appear fused in 3D due to limited resolution. Here, we propose and evaluate several artificial neural network (NN) architectures and conventional image enhancement filters with the aim of solving both problems. To evaluate the effects of filtering, we examine the following four image quality metrics: normalized intensity in the cross-over regions between neurites, radius of neurites, coefficient of variation of intensity along neurites, and local background to neurite intensity ratio. Our results show that NN filters, trained on optimized semi-manual traces of neurites, can significantly outperform conventional filters. In particular, U-Net based filtering can virtually eliminate background intensity, while also reducing radius of neurites by 23% to nearly 1 voxel, decreasing intensity in the cross-over regions between neurites by 22%, and reducing variations in intensity along neurites by 26%. These results suggest that including a NN filtering step, which does not require much extra time or computing power, can be beneficial for neuron tracing projects.