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We propose a novel approach for automatic extraction (instance segmentation)of fibers from low resolution 3D X-ray computed tomography scans of short glassfiber reinforced polymers. We have designed a 3D instance segmentationarchitecture built upon a deep fully convolutional network for semanticsegmentation with an extra output for embedding learning. We show that theembedding learning is capable of learning a mapping of voxels to an embeddedspace in which a standard clustering algorithm can be used to distinguishbetween different instances of an object in a volume. In addition, we discuss amerging post-processing method which makes it possible to process volumes ofany size. The proposed 3D instance segmentation network together with ourmerging algorithm is the first known to authors knowledge procedure thatproduces results good enough, that they can be used for further analysis of lowresolution fiber composites CT scans.

Instance Segmentation of Fibers from Low Resolution CT Scans via 3D Deep Embedding Learning