Survey statistics of automated segmentations applied to optical imaging of mammalian cells
| Autor: | Peter Bajcsy, Michael Halter, Carl G. Simon, Mary Brady, Antonio Cardone, Mylene Simon, Joe Chalfoun, Adele P. Peskin, Antoine Vandecreme, Marcin Kociolek, Michael Majurski, Derek Juba |
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| Jazyk: | angličtina |
| Předmět: |
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Cell segmentation Biochemistry Segmentation evaluation Automation Structural Biology Histogram Animals Humans Computer vision Segmentation Molecular Biology Microscopy Segmentation-based object categorization business.industry Applied Mathematics Segmented objects Optical Imaging Accelerated execution of segmentation for high-throughput biological application Pattern recognition Image segmentation Computer Science Applications Cellular measurements Artificial intelligence business Algorithms Research Article |
| Zdroj: | BMC Bioinformatics |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/s12859-015-0762-2 |
| Popis: | The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements. We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories. The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue. The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html . |
| Databáze: | OpenAIRE |
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