FAST CELL DETECTION IN HIGH-THROUGHPUT IMAGERY USING GPU-ACCELERATED MACHINE LEARNING Conference Paper uri icon

abstract

  • 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. © 2011 IEEE.

author list (cited authors)

  • Mayerich, D., Kwon, J., Panchal, A., Keyser, J., & Choe, Y.

citation count

  • 9

publication date

  • March 2011

publisher