  Constructing 3D structures from serial section data is a long standing
problem in microscopy. The structure of a fiber reinforced composite material
can be reconstructed using a tracking-by-detection model. Tracking-by-detection
algorithms rely heavily on detection accuracy, especially the recall
performance. The state-of-the-art fiber detection algorithms perform well under
ideal conditions, but are not accurate where there are local degradations of
image quality, due to contaminants on the material surface and/or defocus blur.
Convolutional Neural Networks (CNN) could be used for this problem, but would
require a large number of manual annotated fibers, which are not available. We
propose an unsupervised learning method to accurately detect fibers on the
large scale, that is robust against local degradations of image quality. The
proposed method does not require manual annotations, but uses fiber shape/size
priors and spatio-temporal consistency in tracking to simulate the supervision
in the training of the CNN. Experiments show significant improvements over
state-of-the-art fiber detection algorithms together with advanced tracking
performance.
