Advanced Level-Set-Based Cell Tracking in Time-Lapse Fluorescence Microscopy

By Oleh Dzyubachyk, Wiggert A. van Cappellen, Jeroen Essers, Wiro J. Niessen, Erik H. W. Meijering. 2010

290 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Molecular Biology

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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.”
Segmentation and Shape Tracking of Whole Fluorescent Cells Based on the Chan–Vese Model (2013, methodology): “Dzyubachyk (Leiden University Medical Center, The Netherlands) for providing the Matlab interface of the reference tracking algorithm [20].”
Cell Segmentation: 50 Years Down the Road [Life Sciences] (2012, methodology): “The method used here is based on level sets [15].”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, background): “[189] have presented an advanced level set-based cell segmentation and tracking framework by”
An Automatic Learning-Based Framework for Robust Nucleus Segmentation (2016, methodology): “Some improved level set implementation methods have been proposed for fast segmentation, including narrow band [26], graph-vertex coloring [27], convex optimization [28], and more advanced techniques [29].”
Automated and semi‐automated cell tracking: addressing portability challenges (2011, methodology): “Dufour et al. (2005) and Dzyubachyk et al. (2010) use active contours and level-set based methods.”
Methods for cell and particle tracking. (2012, methodology): “In this case, a level-set based model evolution approach was used for segmentation and tracking, modified from Dzyubachyk et al. (2010a). The bottom graph shows the lineage tree automatically derived from the tracking results, with the horizontal guidelines (red, dashed) corresponding to the four ti”
An Objective Comparison of Cell Tracking Algorithms (2017, unknown): “Cell tracking methods can be broadly categorized into two groups: (i) Tracking by contour evolution methods 21,22,24,25 start by segmenting the cells in the first frame of a video and evolve their contours in consecutive frames, thus solving the segmentation and tracking tasks simultaneously, one…”