Image Classification of Vascular Smooth Muscle Cells
Michael Grasso, Ronil Mokashi , Alden A. Dima, Antonio Cardone, Kiran Bhadriraju, Anne L. Plant, Mary C. Brady, Yaacov Yesha, Yelena Yesha
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-cell and cell-to-matrix interactions. Analysis of these changes may be used to better understand how these interactions correspond to certain physiologic processes.
1st ACM International Health Informatics Symposium
, Mokashi, R.
, Dima, A.
, Cardone, A.
, Bhadriraju, K.
, Plant, A.
, Brady, M.
, Yesha, Y.
and Yesha, Y.
Image Classification of Vascular Smooth Muscle Cells, 1st ACM International Health Informatics Symposium , Arlington, VA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=905981
(Accessed December 8, 2023)