Guest Lecture: Artificial Intelligence in Cytometry
Thursday, September, 30th, 2021: 04:30 pm – 05:30 pm
Image analysis and AI in microscopy-based life science research
Dept. of Information Technology and SciLifeLab, Uppsala University, Sweden
Biological processes can be observed both in space and over time using imaging. Visual assessment becomes limiting as datasets grow, and complexity of data as well as subtleness of processes makes it difficult to draw confident conclusions without automated and quantitative measurement strategies. Traditionally, digital image processing has relied on engineering mathematical models of e.g. the size and shape of cell nuclei, surrounding cytoplasm and fluorescent signal distributions, to extract measurements and apply classification strategies. These methods are powerful, but they are also limited by how well we manage to find a good set of feature descriptors for what we observe. In the past ten years, learning-based approaches relying on deep convolutional neural networks (DCNNs) have gained enormous popularity in all fields of image-based science. The methods have great potential, but they also require care in their usage, where again, the traditional image processing methods play crucial role. We develop and apply combinations of traditional and learning-based methods in a range of areas of cytometry, including classification, image registration, ‘virtual staining’, and spatial statistics. One of the bottlenecks of DCNNs is the need for training data, and here we propose interactive approaches for minimal user input in exploring new datasets and new phenotypes. We also show that pre-trained networks can be used as feature extractors, providing valuable quantitative descriptions of e.g. tissue morphology.
Carolina Wählby is professor in Quantitative Microscopy, Dept. of Information Technology, Uppsala University, and director of the National SciLifeLab Bioimage Informatics facility. Her research is focused on developing computational approaches for extracting information from image data with focus on medicine and life science. Methods including large-scale cell-based screening, AI, deep learning for dynamic and static tissue, antibiotics susceptibility testing, and spatial transcriptomics, funded primarily by the ERC and the Swedish Foundation for Strategic research. She received the SBI2 President’s innovation award in 2014, and the Thuréus prize in 2015 and is a member of the Royal Society of Sciences at Uppsala and the Royal Swedish Academy of Engineering Sciences. She has a MSc in molecular biotechnology and a PhD in digital image analysis, and carried out postdoc research within genetics and pathology. She was part of the Imaging Platform of the Broad Institute of Harvard and MIT in 2009-2015, developing CellProfiler, and became full professor at Uppsala University in 2014.
In situ sequencing for RNA analysis in preserved tissue and cells
Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections
Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study