Image classification of vascular smooth muscle cells

Authors:
Michael A. Grasso;Ronil Mokashi;Darshana Dalvi;Antonio Cardone;Alden A. Dima;Kiran Bhadriraju;Anne L. Plant;Mary Brady;Yaacov Yesha;Yelena Yesha
Affiliations:
University of Maryland School of Medicine, Baltimore, MD, USA;University of Maryland Baltimore County, Baltimore, MD, USA;University of Maryland Baltimore County, Baltimore, MD, USA;University of Maryland, College Park, Maryland, USA;National Institute of Standards & Technology, Gaithersburg, MD, USA;National Institute of Standards & Technology, Gaithersburg, MD, USA;National Institute of Standards & Technology, Gaithersburg, MD, USA;National Institute of Standards & Technology, Gaithersburg, MD, USA;University of Maryland Baltimore County, Baltimore, MD, USA;University of Maryland Baltimore County, Baltimore, MD, USA
Venue:
Proceedings of the 1st ACM International Health Informatics Symposium
Year:
2010

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Abstract

The traditional method of cell microscopy can be subjective, due to observer variability, a lack of standardization, and a limited feature set. To address this challenge, we developed an image classifier using a machine learning approach. Our system was able to classify cytoskeletal changes in A10 rat smooth muscle cells with an accuracy of 85% to 99%. These cytoskeletal changes correspond to cell-to-matrix interactions. Analysis of these changes may be used to better understand how these interactions correspond to certain physiologic processes.