Learning to Count Cells: applications to lens-free imaging of large fields
G Flaccavento, Victor Lempitsky, I Pope, P.R. Barber, Andrew Zisserman, J.A. Noble and B Vojnovic
In: MIAAB 2011, September 2, 2011, Heidelberg.

Abstract

We have developed a learning algorithm that counts the number of cells in a large field of view image automatically, and can be used to investigate colony growth in time lapse sequences. The images are acquired using a novel, small, and cost effective diffraction device that can be placed in an incubator during acquisition. This device, termed a CyMap, contains a resonant cavity LED and CMOS camera with no additional optical components or lenses. The counting method is based on structured output learning, and involves segmentation and computation using a random forest. We show that the algorithm can accurately count thousands of cells in a time suitable for immediate analysis of time lapse sequences. Performance is measured using ground truth annotation from registered images acquired under a different modality.

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