Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.01.127035v1?rss=1 Authors: Phan, M.-S., Matho, K. S., Livet, J., Beaurepaire, E., Chessel, A. Abstract: The advent of large-scale microscopy along with advances in automated image analysis algorithms is currently revolutionizing neuroscience. These approaches result in rapidly increasing libraries of neuron reconstructions requiring innovative computational methods to draw biological insight from. Here, we propose a framework from differential geometry based on scale-space representation to extract a quantitative structural readout of neurite traces seen as tridimensional (3D) curves within the anatomical space. We define and propose algorithms to compute a multiscale hierarchical decomposition of traced neurites according to their intrinsic dimensionality, from which we deduce a local 3D scale, i.e. the scale in microns at which the curve is fully 3D as opposed to being embedded in a 2D plane or a 1D line. We applied our scale-space analysis to recently published data including zebrafish whole brain traces to demonstrate the importance of the computed local 3D scale for description and comparison at the single arbor levels and as a local spatialized information characterizing axons populations at the whole brain level. The use of this broadly applicable approach is facilitated through an open-source implementation in Python available through GeNePy3D, a quantitative geometry library. Copy rights belong to original authors. Visit the link for more info