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BibTeX
Automated detection of tunneling nanotubes in 3D images
Cytometry. Part A, 2006-09, Vol.69A (9), p.961-972
Hodneland, Erlend
Lundervold, Arvid
Gurke, Steffen
Tai, Xue‐Cheng
Rustom, Amin
Gerdes, Hans‐Hermann
2006
Volltextzugriff (PDF)
Details
Autor(en) / Beteiligte
Hodneland, Erlend
Lundervold, Arvid
Gurke, Steffen
Tai, Xue‐Cheng
Rustom, Amin
Gerdes, Hans‐Hermann
Titel
Automated detection of tunneling nanotubes in 3D images
Ist Teil von
Cytometry. Part A, 2006-09, Vol.69A (9), p.961-972
Ort / Verlag
Hoboken: Wiley Subscription Services, Inc., A Wiley Company
Erscheinungsjahr
2006
Quelle
Wiley-Blackwell Journals
Beschreibungen/Notizen
Background: This paper presents an automated method for the identification of thin membrane tubes in 3D fluorescence images. These tubes, referred to as tunneling nanotubes (TNTs), are newly discovered intercellular structures that connect living cells through a membrane continuity. TNTs are 50–200 nm in diameter, crossing from one cell to another at their nearest distance. In microscopic images, they are seen as straight lines. It now emerges that the TNTs represent the underlying structure of a new type of cell‐to‐cell communication. Methods: Our approach for the identification of TNTs is based on a combination of biological cell markers and known image processing techniques. Watershed segmentation and edge detectors are used to find cell borders, TNTs, and image artifacts. Mathematical morphology is employed at several stages of the processing chain. Two image channels are used for the calculations to improve classification of watershed regions into cells and background. One image channel displays cell borders and TNTs, the second is used for cell classification and displays the cytoplasmic compartments of the cells. The method for cell segmentation is 3D, and the TNT detection incorporates 3D information using various 2D projections. Results: The TNT‐ and cell‐detection were applied to numerous 3D stacks of images. A success rate of 67% was obtained compared with manual identification of the TNTs. The digitalized results were used to achieve statistical information of selected properties of TNTs. Conclusion: To further explore these structures, automated detection and quantification is desirable. Consequently, this automated recognition tool will be useful in biological studies on cell‐to‐cell communication where TNT quantification is essential. © 2006 International Society for Analytical Cytology
Sprache
Englisch
Identifikatoren
ISSN: 1552-4922
eISSN: 1552-4930
DOI: 10.1002/cyto.a.20302
Titel-ID: cdi_proquest_miscellaneous_68875825
Format
–
Schlagworte
Algorithms
,
Animals
,
Cell Communication
,
cell imaging
,
classification
,
computer vision and recognition
,
image processing
,
Image Processing, Computer-Assisted - methods
,
Imaging, Three-Dimensional - methods
,
mathematical morphology
,
Models, Biological
,
Nanotubes - analysis
,
Nanotubes - ultrastructure
,
PC12 Cells - ultrastructure
,
Rats
,
segmentation
,
tunneling nanotubes
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