Quantifying challenging images of fiber-like structures

We present a practical, parameter-free, general computational-statistical technique for quantitative analysis of 2D images representing fiber-like structures (vessels, neurons, elongated objects, cell boundaries...), which is a common task in many experimental biomedicine scenarios. Our approach does not require segmentation or tracing of fibers; instead, it relies on a learned detector of intersections between fibers and arbitrary segments. The detector´s probabilistic outputs are used to compute an estimate of the density of fibers and of its uncertainty; the latter accounts for several factors, including the intrinsic difficulty of the problem, i.e. the inaccuracy of the detector. After few minutes of training by the user, the procedure performs well in a variety of challenging scenarios, and compares favorably even with problem-specific algorithms.