Identification of Fibers in Micro-CT Images of Paperboard Using Deep Learning

Abstract: This master thesis project explores the possibility of using deep learning to segment individual fibers in three-dimensional tomography images of paperboard fiber networks. We test a method which has previously been used to segment fibers in images of glass fiber reinforced polymers. The method relies on a neural network which produces an embedding for each voxel in the input image, such that the embeddings corresponding to a given fiber should form a cluster in the embedding space. Individual fibers can then be identified by applying a clustering algorithm to the embeddings. Although the method is able to identify some more easily distinguished fibers, the achieved accuracy is insufficient. We find that the main difficulty lies in acquiring training data of high enough quality, and that future work concerning this task is required. In this work, the use of different types of data, including synthetically generated data, and what we refer to as a type of semi-synthetic data, have been tested. Although we do not reach any satisfying results, this work may serve as a base for future research.