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Survey Statistics of Automated Segmentations Applied to Optical Imaging of Mammalian Cells

Published

Author(s)

Peter Bajcsy, Antonio Cardone, Joe Chalfoun, Michael W. Halter, Derek Juba, Marcin Kociolek, Michael P. Majurski, Adele P. Peskin, Carl G. Simon Jr., Mylene H. Simon, Antoine Vandecreme, Anne L. Plant, Mary C. Brady

Abstract

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. 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.
Citation
BMC Bioinformatics

Keywords

Cellular Measurements, Cell Segmentation, Segmented Objects, Segmentation Evaluation, Accelerated Execution of Segmentation for High-throughput Biological Application
Created January 8, 2016, Updated February 19, 2017