Tracking in cell and developmental biology.

By Erik H. W. Meijering, Oleh Dzyubachyk, Ihor Smal, W. V. van Cappellen. 2009

266 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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In Molecular Biology

In Evaluation & Benchmarks

Citations in the corpus

A benchmark for comparison of cell tracking algorithms (2014, methodology): “After the ISBI 2013 workshop, the organizing committee confirmed the accuracy of the submitted results and compiled the final rankings presented in this article.”
Objective comparison of particle tracking methods (2014, background): “Each page shows the results for one density level (Low, Mid, High) and the four different SNR levels (1, 2, 4, 7).”
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.”
Deep learning for cellular image analysis (2019, background): “In linear-programming approaches, multiple cues such as object centroids, intensity, and morphology are combined into a similarity score to link objects between frames 118 .”
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].”
Automated and semi‐automated cell tracking: addressing portability challenges (2011, methodology): “However based on the previous approaches, Meijering et al. (2009) conclude that ‘the consensus arising from the literature seems to be that any specific tracking task requires dedicated (combinations of) algorithms to obtain optimal results’.”
Machine learning applications in cell image analysis (2017, methodology): “16,17 20 This article is centred on ML methods.”
Cell Segmentation: 50 Years Down the Road [Life Sciences] (2012, background): “Level sets have the advantage that they naturally handle topological changes, such as cell division, and are therefore attractive for cell tracking [16].”
Neuron tracing in perspective (2010, methodology): “From general observations in related areas of biomedical image analysis (133) it would seem that improved methods for neuron tracing are possible by bridging the dichotomy of global processing versus local exploration algorithms, exploiting the best of both worlds in a multiscale framework, and…”
Segmentation and Shape Tracking of Whole Fluorescent Cells Based on the Chan–Vese Model (2013, background): “The crucial tasks are, in particular, segmenting, tracking, and evaluating movement tracks and morphological changes of cells, subcellular components, and other particles [5].”