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Fast cell detection in high-throughput imagery using GPU-accelerated machine learning
2011 / IEEE / 978-1-4244-4127-3
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.
Learning (artificial Intelligence)
Medical Image Processing
Real Time Cell Detection
Fast Cell Detection
High Throughput Imagery
Gpu Accelerated Machine Learning
Large Tissue Samples
Data Set Size
Data Set Complexity
Parallel Processing Algorithms
High Throughput Imaging Techniques
Cell Soma Detection
Knife Edge Scanning Microscopy
Graphics Processing Unit
Artificial Neural Networks
Biomedical Optical Imaging
High Throughput Microscopy