A unified framework for automated 3D whole cell segmentation of living cells and a comprehensive segmentation

• Main Authors: Erlend, Hodneland, Nickolay V., Bukoreshtliev, Tilo W., Eichler, Tai, Xue Cheng, Steffen, Gurke, Arvid, Lundervold, Hans-Hermann, Gerdes
• Other Authors: School of Physical and Mathematical Sciences
• Format: Article
• Language: English
• Published: 2009
• Subjects: DRNTU::Science::Mathematics::Applied mathematics; DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
• Online Access: https://hdl.handle.net/10356/91402; http://hdl.handle.net/10220/6055; http://sfxna09.hosted.exlibrisgroup.com:3410/ntu/sfxlcl3?sid=metalib:EBSCO_APH&id=doi:&genre=&isbn=&issn=02780062&date=2009&volume=28&issue=5&spage=720&epage=738&aulast=Hodneland&aufirst=%20Erlend&auinit=&title=IEEE%20Transactions%20on%20Medical%20Imaging&atitle=A%20Unified%20Framework%20for%20Automated%203%2DD%20Segmentation%20of%20Surface%2DStained%20Living%20Cells%20and%20a%20Comprehensive%20Segmentation%20Evaluation%2E&sici
• Institution: Nanyang Technological University

This work presents a unified framework for whole cell segmentation of surface stained living cells from 3-D data sets of fluorescent images. Every step of the process is described, image acquisition, prefiltering, ridge enhancement, cell segmentation, and a segmentation evaluation. The segmentation results from two different automated approaches for segmentation are compared to manual segmentation of the same data using a rigorous evaluation scheme. This revealed that combination of the respective cell types with the most suitable microscopy method resulted in high success rates up to 97%. The described approach permits to automatically perform a statistical analysis of various parameters from living cells.