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Constructing 3D structures from serial section data is a long standingproblem in microscopy. The structure of a fiber reinforced composite materialcan be reconstructed using a tracking-by-detection model. Tracking-by-detectionalgorithms rely heavily on detection accuracy, especially the recallperformance. The state-of-the-art fiber detection algorithms perform well underideal conditions, but are not accurate where there are local degradations ofimage quality, due to contaminants on the material surface and/or defocus blur.Convolutional Neural Networks (CNN) could be used for this problem, but wouldrequire a large number of manual annotated fibers, which are not available. Wepropose an unsupervised learning method to accurately detect fibers on thelarge scale, that is robust against local degradations of image quality. Theproposed method does not require manual annotations, but uses fiber shape/sizepriors and spatio-temporal consistency in tracking to simulate the supervisionin the training of the CNN. Experiments show significant improvements overstate-of-the-art fiber detection algorithms together with advanced trackingperformance.

Unsupervised Learning for Large-Scale Fiber Detection and Tracking in Microscopic Material Images