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

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.

name of conference

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

authors

• Choe, Yoonsuck
• Keyser, John

published proceedings

• 2011 8TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO

author list (cited authors)

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

citation count

• 10

complete list of authors

• Mayerich, David||Kwon, Jaerock||Panchal, Aaron||Keyser, John||Choe, Yoonsuck

publication date

• January 2011

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging  Journal

keywords

• Cell Soma
• Microscopy
• Seed Points
• Segmentation
• Three-dimensional

Digital Object Identifier (DOI)

• 10.1109/isbi.2011.5872507

International Standard Book Number (ISBN) 13

• 9781424441280

start page

• 719

end page

• 723

volume

• 1

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fisbi.2011.5872507