title: Learning to detect cells using non-overlapping extremal regions.
abstract: 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.
authors.affiliation: Department of Engineering Science, University of Oxford, UK.
authors.forename: Carlos
authors.initials: C
authors.lastname: Arteta
authors.forename: Victor
authors.initials: V
authors.lastname: Lempitsky
authors.forename: J Alison
authors.initials: JA
authors.lastname: Noble
authors.forename: Andrew
authors.initials: A
authors.lastname: Zisserman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D048429:Cell Size; D003198:Computer Simulation; D006367:HeLa Cells; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods; D008858:Microscopy, Phase-Contrast / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012984:Software; D060388:Support Vector Machine