Prof. Kevin W. Eliceiri

Fellow Member | RRF Walter H. Helmerich Professor Medical Physics at Univ of Wisconsin
Eliceiri, Kevin W.
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SPIE Membership: 10.9 years
SPIE Awards: Fellow status | Senior status | 2020 SPIE Community Champion | 2019 SPIE Community Champion
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Social Media: LinkedIn
ORCID iD: https://orcid.org/0000-0001-8678-670X
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Profile Summary

Dr. Eliceiri has been conducting research on computational and optical approaches to live cell imaging for two decades, with a focus on multidimensional fluorescence approaches for cell studies and image informatics tools for image analysis. Under his NIH P41 funded “Center for Open Bioimage Analysis” (COBA) his lab develops and maintain the open-source software FIJI and ImageJ while making new deep learning tools and workflow solutions for bioimaging. A major focus of his research is to develop and apply non-invasive optical and computational approaches for understanding the role of the cellular microenvironment in normal and diseased processes. The Eliceiri lab has developed a wide range of second harmonic generation, spectral, lifetime and polarization based imaging methods for investigating cell-cell, cell-matrix and endothelial-stromal interactions and metabolism. Dr. Eliceiri and collaborators demonstrated that alignment of collagen fibers surrounding tumor epithelial cells can serve as a quantitative image-based biomarker for survival of invasive ductal breast carcinoma patients. Specific types of collagen alignment have been identified for their prognostic value in a wide range of cancers and now these tumor associated collagen signatures (TACS) are central to several clinical specimen imaging trials. The Eliceiri lab has also developed specialized open-source tools for collagen quantification that can measure not only the individual collagen fiber geometries like thickness, length, angle, and curvature but also bulk properties like global or local fiber density and alignment. More recently they have been developing automated machine learning approaches to automatically recognize cell-stroma patterns in large histopathology datasets.





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