PUBLICATIONS
“The joy of discovery is certainly the liveliest that the mind of man can ever feel”
- Claude Bernard -
Conference Publications
Abstract: Examining specific sub-cellular structures while minimizing cell perturbation is important in the life sciences. Fluorescence labeling and imaging is widely used for introducing specificity despite its perturbative and photo-toxic nature.With the advancement of deep learning, digital staining routines for label-free analysis have emerged as a replacement for fluorescence imaging. Nonetheless, digital staining of sub-cellular structures such as mitochondria is sub-optimal. This is because the models designed for computer vision are directly applied instead of optimizing them for the nature of microscopy data. We propose a new loss function with multiple thresholding steps to promote more effective learning for microscopy data. Through this, we demonstrate a deep learning approach to translate the label-free brightfield images of living cells into equivalent fluorescence images of mitochondria with an average structural similarity of 0.77, thus surpassing the state-of-the-art of 0.7 with L1. Our results provide insightful examples of some unique opportunities generated by data-driven deep-learning enabled image translations.
Meet The Research Team
3dNanoscopy
Our research lies at the nexus of physics, mathematics, and engineering, for developing revolutionary imaging solutions that aid research in clinical biology and medicine. We believe that good teams make great science.
Virtual Stain
VirtualStain is an ambitious project that could have a far-reaching impact on the way we analyse and interpret tissue- and cell-images. This large, collaborative effort, involving four departments from three different faculties, is part of the UiT Tematiske satsninger, a funding program intended to encourage innovative interdepartmental and interdisciplinary projects.
Nanoscopy
Rising up from a (formerly) small integrated optics group, ERC grant jump-started the Nanoscopy group in 2015. Our team is made up of physicists, computer-scientists, engineers and biologists with research topics stretching from advanced imaging of cells and tissues via image processing to the development of the emerging field of chip-based nanoscopy.