Dag Kristian Dysthe

This paper introduces a cellular automaton model to study osteogenic differentiation and shows endpoint assays have limited ability to distinguish underlying mechanisms.

This paper uses quantitative phase imaging to study 3D dynamics of epithelial monolayers, revealing mass conservation and non-prismatic cell geometry challenges to 2.5D assumptions.

This paper develops a deep learning model to predict stem cell efficiency for beta cell differentiation using phase-contrast images, achieving high accuracy in clone classification.

This study measures height, volume, and mass dynamics of MDCK monolayers using two QPI techniques and develops new methods for cell segmentation and height statistics without image segmentation.