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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Learning to Detect Cells Using Non-overlapping Extremal Regions
verfasst von : Carlos Arteta, Victor Lempitsky, J. Alison Noble, Andrew Zisserman
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012
Verlag: Springer Berlin Heidelberg
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Cell detection in microscopy images is an important step in the automation of cell based-experiments. We propose a machine learning-based cell detection method applicable to different modalities. The method consists of three steps: first, a set of candidate cell-like regions is identified. Then, each candidate region is evaluated using a statistical model of the cell appearance. Finally, dynamic programming picks a set of non-overlapping regions that match the model. The cell model requires few images with simple dot annotation for training and can be learned within a structured SVM framework. In the reported experiments, state-of-the-art cell detection accuracy is achieved for H&E-stained histology, fluorescence, and phase-contrast images.