Algorithms for cytoplasm segmentation of fluorescence labelled cells.
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.
Wählby
Carolina
C
Centre for Image Analysis at Uppsala University, Uppsala, Sweden. carolina@cb.uu.se
Lindblad
Joakim
J
Vondrus
Mikael
M
Bengtsson
Ewert
E
Björkesten
Lennart
L
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