title: Algorithms for cytoplasm segmentation of fluorescence labelled cells.
abstract: Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre-processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO-cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.
authors.affiliation: Centre for Image Analysis at Uppsala University, Uppsala, Sweden. carolina@cb.uu.se
authors.forename: Carolina
authors.initials: C
authors.lastname: Wählby
authors.forename: Joakim
authors.initials: J
authors.lastname: Lindblad
authors.forename: Mikael
authors.initials: M
authors.lastname: Vondrus
authors.forename: Ewert
authors.initials: E
authors.lastname: Bengtsson
authors.forename: Lennart
authors.initials: L
authors.lastname: Björkesten
mesh_terms: D000465:Algorithms; D000818:Animals; D003593:Cytoplasm / Q000648:ultrastructure*; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008856:Microscopy, Fluorescence / Q000379:methods