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Fast cell detection in high-throughput imagery using GPU-accelerated machine learning

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5 Author(s)
David Mayerich ; Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, USA ; Jaerock Kwon ; Aaron Panchal ; John Keyser
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High-throughput microscopy allows fast imaging of large tissue samples, producing an unprecedented amount of sub-cellular information. The size and complexity of these data sets often out-scale current reconstruction algorithms. Overcoming this computational bottleneck requires extensive parallel processing and scalable algorithms. As high-throughput imaging techniques move into main stream research, processing must also be inexpensive and easily available. In this paper, we describe a method for cell soma detection in Knife-Edge Scanning Microscopy (KESM) using machine learning. The proposed method requires very little training data and can be mapped to consumer graphics hardware, allowing us to perform real-time cell detection at a rate that exceeds the data rate of KESM.

Published in:

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Date of Conference:

March 30 2011-April 2 2011