Algorithms for cytoplasm segmentation of fluorescence labeled cells

Wählby, Carolina (author): Uppsala universitet,Centrum för bildanalys,Datoriserad bildanalys

Lindblad, Joakim (author): Uppsala universitet,Centrum för bildanalys,Datoriserad bildanalys

Vondrus, Mikael (author)

Bengtsson, Ewert (author)

Björkesten, Lennart (author)

(creator_code:org_t)

2002
2002
English.
In: Analytical Cellular Pathology. - 0921-8912 .- 1878-3651. ; 24:2-3, s. 101-111

Abstract Subject headings

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.

By the author/editor: Wählby, Carolina; Lindblad, Joakim; Vondrus, Mikael; Bengtsson, Ewert; Björkesten, Lenn ...

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