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

David Mayerich, Jaerock Kwon, Aaron Panchal, John Keyser, Yoonsuck Choe
Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, USA
IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011

@inproceedings{mayerich2011fast,

   title={Fast cell detection in high-throughput imagery using GPU-accelerated machine learning},

   author={Mayerich, D. and Kwon, J. and Panchal, A. and Keyser, J. and Choe, Y.},

   booktitle={Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on},

   pages={719–723},

   year={2011},

   organization={IEEE}

}

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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.
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