Master's thesis: Segmentation of Liquid Foam bubbles in Volumetric X-Ray Computed Tomography Datasets Using Deep Learning

Background

The thesis forms part of the VINNOVA financed project “”. The goal of AI-TOMO is to develop AI algorithms for fast, effective segmentation and quantification of 3D and 4D X-ray tomography data to accelerate materials development. AI-TOMO is a close collaboration between the research providers RISE and Lund University, the synchrotron facility MAX IV, and the companies Billerud and TetraPak. This master thesis will be a collaboration between AI researchers at Lund University and RISE with support from X-ray tomography experts at Lund University.

Liquid foam is extensively used in daily life and in industry for its peculiar solid-liquid behavior: from shaving foam, whipped cream, to large-scale processes such as soil remediation, water treatment and paper recycling. Its well-defined microstructure makes it an ideal model system for studying structural phenomena shared with other disordered materials, such as glasses and biological tissues.

This proj...