Towards the Automated Detection and Characterization of Osteoclasts in Microscopic Images

Microscopes have been used for a long time to observe biological samples. However, measurements of tissue- and cell-related parameters were conducted by human observers and were consequently ad hoc, not reproducible and restricted to small sample numbers. Since computers have become vastly more powerful, life sciences now routinely take advantage of new opportunities to couple microscopy and in silico methods. Automated image segmentation and analysis of large numbers of digital images allow algorithmic recognition of cell and tissue structures and subsequent numeric measurements of cellular parameters. Nevertheless, these new methods also come with technical challenges concerning computational resources like processing capacity, memory and disk space, biological sensor limitations, as well as algorithm development.

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