Abstract
BackgroundWith the increasing availability of live cell imaging technology, tracking cells and other moving objects in live cell videos has become a major challenge for bioimage informatics. An inherent problem for most cell tracking algorithms is over- or under-segmentation of cells – many algorithms tend to recognize one cell as several cells or vice versa.ResultsWe propose to approach this problem through so-called topological alignments, which we apply to address the problem of linking segmentations of two consecutive frames in the video sequence. Starting from the output of a conventional segmentation procedure, we align pairs of consecutive frames through assigning sets of segments in one frame to sets of segments in the next frame. We achieve this through finding maximum weighted solutions to a generalized "bipartite matching" between two hierarchies of segments, where we derive weights from relative overlap scores of convex hulls of sets of segments. For solving the matching task, we rely on an integer linear program.ConclusionPractical experiments demonstrate that the matching task can be solved efficiently in practice, and that our method is both effective and useful for tracking cells in data sets derived from a so-called Large Scale Digital Cell Analysis System (LSDCAS).AvailabilityThe source code of the implementation is available for download from http://www.picb.ac.cn/patterns/Software/topaln.
Highlights
With the increasing availability of live cell imaging technology, tracking cells and other moving objects in live cell videos has become a major challenge for bioimage informatics
While cell tracking algorithms can build on a rich pool of image processing methods that have been developed in the context of other motion tracking problems, biological images contain their own intricacies
We primarily aim to measure the quality of the topological alignments computed in the linking stage
Summary
With the increasing availability of live cell imaging technology, tracking cells and other moving objects in live cell videos has become a major challenge for bioimage informatics. Studying cell motility has become an important factor in understanding numerous biological processes, driven by the rapid development of bio-imaging technology. While cell tracking algorithms can build on a rich pool of image processing methods that have been developed in the context of other motion tracking problems, biological images contain their own intricacies. Bioimage data are captured in order to quantify phenomena such as cell division or cell fusion. Such events are difficult to recognize computationally, in particular when dealing with 2D images of a tissue or cell culture that hides essential 3D information and contains a large number of cells. Further challenges in biological image processing are inherently low contrast images and cells changing their shape or momentum abruptly
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