Quantitative segmentation of fluorescence microscopy images of heterogeneous tissue: Approach for tuning algorithm parameters

Jenna L. Mueller; Zachary T. Harmany; Jeffrey K. Mito; Stephanie A. Kennedy; Yongbaek Kim; Leslie Dodd; Joseph Geradts; David G. Kirsch; Rebecca M. Willett; J. Quincy Brown; Nimmi Ramanujam

doi:10.1117/12.2006429

22 February 2013 Quantitative segmentation of fluorescence microscopy images of heterogeneous tissue: Approach for tuning algorithm parameters

Jenna L. Mueller, Zachary T. Harmany, Jeffrey K. Mito, Stephanie A. Kennedy, Yongbaek Kim, Leslie Dodd, Joseph Geradts, David G. Kirsch, Rebecca M. Willett, J. Quincy Brown, Nimmi Ramanujam

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Proceedings Volume 8587, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XI; 85871F (2013) https://doi.org/10.1117/12.2006429
Event: SPIE BiOS, 2013, San Francisco, California, United States

Abstract

The combination of fluorescent contrast agents with microscopy is a powerful technique to obtain real time images of tissue histology without the need for fixing, sectioning, and staining. The potential of this technology lies in the identification of robust methods for image segmentation and quantitation, particularly in heterogeneous tissues. Our solution is to apply sparse decomposition (SD) to monochrome images of fluorescently-stained microanatomy to segment and quantify distinct tissue types. The clinical utility of our approach is demonstrated by imaging excised margins in a cohort of mice after surgical resection of a sarcoma. Representative images of excised margins were used to optimize the formulation of SD and tune parameters associated with the algorithm. Our results demonstrate that SD is a robust solution that can advance vital fluorescence microscopy as a clinically significant technology.

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Jenna L. Mueller, Zachary T. Harmany, Jeffrey K. Mito, Stephanie A. Kennedy, Yongbaek Kim, Leslie Dodd, Joseph Geradts, David G. Kirsch, Rebecca M. Willett, J. Quincy Brown, and Nimmi Ramanujam "Quantitative segmentation of fluorescence microscopy images of heterogeneous tissue: Approach for tuning algorithm parameters ", Proc. SPIE 8587, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XI, 85871F (22 February 2013); https://doi.org/10.1117/12.2006429

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KEYWORDS

Tissues

Image segmentation

Tumors

Microscopy

Luminescence

Medicine

Pathology

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