Abstract：
:Cell segmentation and tracking in time-lapse fluorescence microscopy images is a task of fundamental importance in many biological studies on cell migration and proliferation. In recent years, level sets have been shown to provide a very appropriate framework for this purpose, as they are well suited to capture topological changes occurring during mitosis, and they easily extend to higher dimensional image data. This model evolution approach has also been extended to deal with many cells concurrently. Notwithstanding its high potential, the multiple-level-set method suffers from a number of shortcomings, which limit its applicability to a larger variety of cell biological imaging studies. In this paper, we propose several modifications and extensions to the coupled-active-surfaces algorithm, which considerably improve its robustness and applicability. Our algorithm was validated by comparing it to the original algorithm and two other cell segmentation algorithms. For the evaluation, four real fluorescence microscopy image datasets were used, involving different cell types and labelings that are representative of a large range of biological experiments. Improved tracking performance in terms of precision (up to 11%), recall (up to 8%), ability to correctly capture all cell division events, and computation time (up to nine times reduction) is achieved.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Dzyubachyk O,van Cappellen WA,Essers J,Niessen WJ,Meijering Edoi
10.1109/TMI.2009.2038693subject
Has Abstractpub_date
2010-03-01 00:00:00pages
852-67issue
3eissn
0278-0062issn
1558-254Xjournal_volume
29pub_type
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