Carolina Wählby is a professor in quantitative microscopy at the Dept. of Information Technology at UU. Her research group is focused on developing computational approaches for extracting information from microscopy images. Images typically come from experiments aimed to understand biology or diagnose disease, and projects range from large-scale cell-based screens for drug development to AI, deep learning, and morphological methods analysis of tissue and decision support in digital pathology.
Keywords: life science electron microscopy bildanalys datoriserad bildanalys quantitative methods free and open source software artificial intelligence image analysis algorithms informationsteknologi fluorescence microscopy light microscopy deep learning scilifelab digital pathology digital patologi
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I received a MSc in Molecular Biothechnology in 1998, and during my MSc thesis work at the Karolinska Institute I was fascinated by how cells can be studied using microscopy, and continued as a PhD student in digital image processing at Uppsala University, focusing on methods for finding cells and extracting quantitative measurements from digital microscopy data. After completeing my PhD in 2003, I did a postdoc in genetics and pathology, with emphasis on methods development. I joined the Broad Institute of Harvard and MIT i USA in 2009, and worked with algorithms for analysis of large scale experiments on model organisms such as C. elegans worms and zebrafish to evaluate the effect of new potential drugs. I returned to Sweden and SciLifeLab and became full professor in quantitative microscopy at the centre for image analysis, Dept. of Information Technology, in 2014. My reserach group develops digital image processing and analysis methods for analysis of different types of microscopy image data with applications in the life sciences and medicine, both basic and more application oriented research.
More information is available on my lab web page.
Digital image processing and analysis is all about interpreting image data using a computer. As an example, we can train a computer to recognize different family members from our holiday photos or automatically decipher the license plate of a car at a car toll. My research group develops digital image analysis methods for automated analysis and extraction of quantitative information from digital image data collected via different types of microscopy. The goal of the analysis is often to measure changes in color, shape, pattern or size from large numbers of images collected by automated microscopy systems, for example to evaluate how different drugs affect cells or model organisms in a laboratory environment. We also quantify morphological changes in tissue samples, using deep convolutional neural networks (a branch of AI, artificial intelligence) aiming to diagnose disease or better understand how the body responds to different treatments. We collaborate with researchers from the life sciences and medicine, and develop methods that can answer important questions in a robust, fast, and reproducible way.
More information can be found on my lab web page.
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