Your Search Results

Use this resource - and many more! - in your textbook!

AcademicPub holds over eight million pieces of educational content for you to mix-and-match your way.

Experience the freedom of customizing your course pack with AcademicPub!
Not an educator but still interested in using this content? No problem! Visit our provider's page to contact the publisher and get permission directly.

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

By: Keyser, J.; Panchal, A.; Jaerock Kwon; Mayerich, D.; Yoonsuck Choe;

2011 / IEEE / 978-1-4244-4127-3

Description

This item was taken from the IEEE Conference ' Fast cell detection in high-throughput imagery using GPU-accelerated machine learning ' 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.