Cell population tracking and lineage construction with spatiotemporal context

By Kang Li, Eric D. Miller, Mei Chen, Takeo Kanade, L. Weiss, P. Campbell. 2008

415 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

A benchmark for comparison of cell tracking algorithms (2014, methodology): “…cells, represented either parametrically (Dufour et al. , 2011; Zimmer et al. , 2002) or implicitly (Dufour et al. , 2005; Dzyubachyk et al. , 2010; Li et al. , 2008; Masˇka et al. , 2013), using a velocity term defined by the content of the ‘‘target’’ frame (e.g. gradient features or intra-and…”
Machine learning applications in cell image analysis (2017, background): “59 Relevant to that, exploring more ﬂ exible forms of incorporating available information (apart from training data) could lead to further progress.”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, background): “[228] have combined a region energy [229] and an edge energy [222] into a level set model to segment and track cells in phase-”
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking (2015, background): “Sequences of such scenarios do exist in the literature, e.g. thousands of bats ﬁlmed using thermal cameras [53], cell tracking data [32], basketball game [11], hockey Conﬁdence score Indicates how conﬁdent the detector is that this instance is a pedestrian.”
Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies (2017, background): “It covers a wide range of applications such as sports analysis [40, 47, 70], biology (birds [41], ants [27], ﬁshes [62, 63, 14], cells [42, 36]), robot navigation [11, 12], and autonomous driving vehicles [13, 53].”
Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis (2011, background): “Challenge Contour Stochastic Segment & Evolution Filtering Associate Mitosis & Merging + Appearing & Disappearing Low Temporal Resolution + + Fast Motion + + Tracking Drift + Accurate Segmentation + + Selected References [6, 7, 8, 9] [10, 11, 12, 13] [14, 15, 16, 17]”
Automated and semi‐automated cell tracking: addressing portability challenges (2011, methodology): “, 2009), whereas the second measures the ratio of the correctly reconstructed tracks (Li et al., 2008).”
Tracking in cell and developmental biology. (2009, background): “Another trend in the attempt to achieve more robustness is the use of probabilistic schemes [31] [42] [44] [54] .”
Advanced Level-Set-Based Cell Tracking in Time-Lapse Fluorescence Microscopy (2010, background): “Time-lapse studies of live cells have aimed at studying cell migration, cell cycle [12], [13], [17] and growth control [14], proliferation [15], [18], [ 19 ], adhesion [16], and many other functions, under various conditions.”
Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data (2014, neutral): “In general, computational approaches to cell segmentation and tracking can be divided into three main categories: contour evolution (for example, level sets) 21 , state-space models (for example, particle filters) 22 and data association methods (for example, graph matching) 20,23 .”
Segmentation and Shape Tracking of Whole Fluorescent Cells Based on the Chan–Vese Model (2013, background): “Approaches developed for this specific task can broadly be classified [9] as either tracking by detection [10]–[13] or tracking by model evolution [9], [14]–[21].”
Multiple object tracking: A literature review (2014, background): “Objects to track can be, for example, pedestrians on the street [1], [2], vehicles in the road [3], [4], sport players on the court [5], [6], [7], or groups of animals (birds [8], bats [9], ants [10], fish [11], [12], [13], cells [14], [15], etc.”
Reliable cell tracking by global data association (2011, methodology): “[2] associate new cells with its parent cell as daughter cells by using a local association method.”
An Objective Comparison of Cell Tracking Algorithms (2017, neutral): “Complete Tracks (CT) 39 : CT examines how good a method is at reconstructing complete reference tracks (i.e., the tracks in TRA-GT ).”
A Novel Validation Algorithm Allows for Automated Cell Tracking and the Extraction of Biologically Meaningful Parameters (2011, methodology): “…cell [14,15], while more complex approaches use specific models to describe the similarity of a cell with itself, such as image registration [16] or smooth shape transitions (active contours [10,17,18,19,20]) and markov models based on simultaneous changes of a variety of cell features [14].”