Survey statistics of automated segmentations applied to optical imaging of mammalian cells

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Fig. 1

Segmentation labels ranging from generic (foreground, background) to cell specific objects relevant to diffraction-limited microscopy (DNA/RNA, protein, organelle, or cytoskeleton)

Fig. 2

Top: The pipeline for an imaging measurement. Bottom: Different types of reference materials that can be used to evaluate performance of the different stages of the measurement pipeline

Fig. 3

Survey organization of the Results section with respect to the imaging measurement pipeline. Four sections are devoted quality of segmentation inputs (Experimental inputs to cell imaging and segmentation), automation (Design of automated segmentation algorithms), evaluation (Evaluations of automated segmentations) and computational scalability (Scalability of automated segmentations)

Fig. 4

Taxonomy of image segmentation methods for mammalian cells

Fig. 5

A histogram of the number of evaluation objects used in surveyed papers that reported segmentation evaluation