Our main research theme is empowering biologists with easy-to-use, comprehensively documented tools that facilitate discovery from biological images. From organelles to organisms, we create tools and workflows that allow biologists to find the objects they care about in microscopy images and generate the measurements they need to answer their research questions.
Research
CellProfiler and other bioimage analysis software
Algorithms developed in my group are made readily usable by the scientific community via our user-friendly software, CellProfiler and CellProfiler Analyst (cellprofiler.org). CellProfiler is versatile, open-source software for quantifying a variety of phenotypes in biological images. Since its release in 2005, it has become well established and widely used by thousands of biologists worldwide [citations]. The software evolves within an active research environment involving dozens of diverse image-based assays, resulting in rich functionality as we continue to improve its capabilities, interface, and support.
We are now leading the community to bridge the gap between biologists and advancements in deep learning. Our first major projects in this area are Piximi, for cell classification, and the Data Science Bowl, which yielded a robust trained model to segment nuclei in images, across diverse microscopy types and cell types.
Image analysis for high-content screens
Example projects include the identification of genetic regulators (glioblastoma differentiation, breast cancer cells' response to heregulin, meiosis) and chemical regulators (leukemic differentiation, mitochondrial function, tuberculosis infection).