Cell Segmentation: 50 Years Down the Road [Life Sciences]

By Erik H. W. Meijering. 2012

521 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Media Technology, Molecular Biology, Medical Imaging

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Citations in the corpus

Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy (2019, background): “Detection and segmentation of cell nuclei in volumetric (3D) fluorescence microscopy images is a ubiquitous problem in developmental biology and often constitutes the first step when studying cellular expression patterns, or when tracing cell lineages in developing organisms [17, 22].”
An Objective Comparison of Cell Tracking Algorithms (2017, unknown): “The image processing community has addressed the above-mentioned tasks using increasingly sophisticated segmentation and tracking algorithms 5–7 .”
An Automatic Learning-Based Framework for Robust Nucleus Segmentation (2016, background): “Recent literature [1], [2] shows that computer-aid image analysis can significantly improve the objectivity and reproducibility.”
The ImageJ ecosystem: Open‐source software for image visualization, processing, and analysis (2020, background): “29,67,68 Objects or structures of interest are separated from the background signal and then can be tracked frame-to-frame.”
Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images (2018, background): “One critical step in quantifying ﬂ uorescence images is often the identi ﬁ cation of the nucleus of each cell with a DNA stain, and there is a long history of research efforts to design and improve nuclear and cellular segmentation (6).”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, background): “Watershed transform is the most popular region accumulation method, which starts from certain seed points and iteratively adds connected pixels to create labeled regions [201].”
Computer vision for high content screening (2016, methodology): “Image-filtering techniques identify patterns such as edges or blobs in images using classical filters such as Gaussian filters and edge detectors (Meijering, 2012).”
Machine learning and computer vision approaches for phenotypic profiling (2017, background): “Toward the generation of phenotypic profiles, individual objects of interest, such as cells or whole organisms, must first be identified and distinguished from the background of an image, in a process called segmentation (Bengtsson et al., 2004; Meijering, 2012; Kraus and Frey, 2016).”