Adele P. Peskin

Comparison of Segmentation Algorithms For Fluorescence Microscopy Images of Cells

The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold‐based segmentation techniques are less accurate than k‐means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability. Published 2011 Wiley‐Liss, Inc.

Ion-exchanged waveguide lasers in Er 3+ /Yb 3+ codoped silicate glass

We investigated an Er(3+)/Yb(3+) codoped silicate glass as a host material for waveguide lasers operating near 1.5 microm. Spectroscopic properties of the glass are reported. Waveguide lasers were fabricated by K(+)-ion exchange from a nitrate melt. The waveguides support a single transverse mode at 1.5 microm. An investigation of the laser performance as a function of the Yb:Er ratio was performed, indicating an optimal ratio of approximately 5:1. Slope efficiencies of as great as 6.5% and output powers as high as 19.6 mW at 1.54 microm were realized. The experimental results are compared with a waveguide laser model that is used to extract the Er(3+) upconversion coefficients and the Yb(3+)-Er(3+) cross-relaxation coefficients. The results indicate the possibility of obtaining high-performance waveguide lasers from a durable silicate host glass.

Accelerating Scientific Discovery Through Computation and Visualization II

The rate of scientific discovery can be accelerated through computation and visualization. This acceleration results from the synergy of expertise, computing tools, and hardware for enabling high-performance computation, information science, and visualization that is provided by a team of computation and visualization scientists collaborating in a peer-to-peer effort with the research scientists. In the context of this discussion, high performance refers to capabilities beyond the current state of the art in desktop computing. To be effective in this arena, a team comprising a critical mass of talent, parallel computing techniques, visualization algorithms, advanced visualization hardware, and a recurring investment is required to stay beyond the desktop capabilities. This article describes, through examples, how the Scientific Applications and Visualization Group (SAVG) at NIST has utilized high performance parallel computing and visualization to accelerate condensate modeling, (2) fluid flow in porous materials and in other complex geometries, (3) flows in suspensions, (4) x-ray absorption, (5) dielectric breakdown modeling, and (6) dendritic growth in alloys.

Segmenting time-lapse phase contrast images of adjacent NIH 3T3 cells

We present a new method for segmenting phase contrast images of NIH 3T3 fibroblast cells that is accurate even when cells are physically in contact with each other. The problem of segmentation, when cells are in contact, poses a challenge to the accurate automation of cell counting, tracking and lineage modelling in cell biology. The segmentation method presented in this paper consists of (1) background reconstruction to obtain noise‐free foreground pixels and (2) incorporation of biological insight about dividing and nondividing cells into the segmentation process to achieve reliable separation of foreground pixels defined as pixels associated with individual cells. The segmentation results for a time‐lapse image stack were compared against 238 manually segmented images (8219 cells) provided by experts, which we consider as reference data. We chose two metrics to measure the accuracy of segmentation: the ‘Adjusted Rand Index’ which compares similarities at a pixel level between masks resulting from manual and automated segmentation, and the ‘Number of Cells per Field’ (NCF) which compares the number of cells identified in the field by manual versus automated analysis. Our results show that the automated segmentation compared to manual segmentation has an average adjusted rand index of 0.96 (1 being a perfect match), with a standard deviation of 0.03, and an average difference of the two numbers of cells per field equal to 5.39% with a standard deviation of 4.6%.

Measurement Tools for the Immersive Visualization Environment: Steps Toward the Virtual Laboratory

This paper describes a set of tools for performing measurements of objects in a virtual reality based immersive visualization environment. These tools enable the use of the immersive environment as an instrument for extracting quantitative information from data representations that hitherto had be used solely for qualitative examination. We provide, within the virtual environment, ways for the user to analyze and interact with the quantitative data generated. We describe results generated by these methods to obtain dimensional descriptors of tissue engineered medical products. We regard this toolbox as our first step in the implementation of a virtual measurement laboratory within an immersive visualization environment.

FogBank: a single cell segmentation across multiple cell lines and image modalities

BackgroundMany cell lines currently used in medical research, such as cancer cells or stem cells, grow in confluent sheets or colonies. The biology of individual cells provide valuable information, thus the separation of touching cells in these microscopy images is critical for counting, identification and measurement of individual cells. Over-segmentation of single cells continues to be a major problem for methods based on morphological watershed due to the high level of noise in microscopy cell images. There is a need for a new segmentation method that is robust over a wide variety of biological images and can accurately separate individual cells even in challenging datasets such as confluent sheets or colonies.ResultsWe present a new automated segmentation method called FogBank that accurately separates cells when confluent and touching each other. This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images. The method is based on morphological watershed principles with two new features to improve accuracy and minimize over-segmentation.First, FogBank uses histogram binning to quantize pixel intensities which minimizes the image noise that causes over-segmentation. Second, FogBank uses a geodesic distance mask derived from raw images to detect the shapes of individual cells, in contrast to the more linear cell edges that other watershed-like algorithms produce.We evaluated the segmentation accuracy against manually segmented datasets using two metrics. FogBank achieved segmentation accuracy on the order of 0.75 (1 being a perfect match). We compared our method with other available segmentation techniques in term of achieved performance over the reference data sets. FogBank outperformed all related algorithms. The accuracy has also been visually verified on data sets with 14 cell lines across 3 imaging modalities leading to 876 segmentation evaluation images.ConclusionsFogBank produces single cell segmentation from confluent cell sheets with high accuracy. It can be applied to microscopy images of multiple cell lines and a variety of imaging modalities. The code for the segmentation method is available as open-source and includes a Graphical User Interface for user friendly execution.

An object-oriented approach to general purpose fluid dynamics software☆

Abstract Our objective is to demonstrate the efficacy of object-oriented programming for the development of scientific and engineering analysis software. We illustrate this through discussion of our application of object-oriented programming to computational fluid dynamics. We have written an object-oriented code in C++ employing the finite element method for the solution of transportphenomena problems. Our emphasis is chemical engineering applications. The code solves the continuum equations of fluid dynamics, heat transfer and mass transfer with chemical reactions. In principle, the number of chemical reactions is limited only by the computing facilities that are available. New reactions are easily added to the system via the input data to the code. To demonstrte the code, we present original research simulating electroplating into a small pit with a fluid jet traversing the top, as occurs in circuit-board manufacture. We used a fully coupled transient model of the fluid dynamics and electrochemistry, and tracked the growth of the electroplated surface. We describe how this work was facilitated by the use of object-oriented programming.

Empirical Gradient Threshold Technique for Automated Segmentation across Image Modalities and Cell Lines

New microscopy technologies are enabling image acquisition of terabyte‐sized data sets consisting of hundreds of thousands of images. In order to retrieve and analyze the biological information in these large data sets, segmentation is needed to detect the regions containing cells or cell colonies. Our work with hundreds of large images (each 21 000×21 000 pixels) requires a segmentation method that: (1) yields high segmentation accuracy, (2) is applicable to multiple cell lines with various densities of cells and cell colonies, and several imaging modalities, (3) can process large data sets in a timely manner, (4) has a low memory footprint and (5) has a small number of user‐set parameters that do not require adjustment during the segmentation of large image sets. None of the currently available segmentation methods meet all these requirements. Segmentation based on image gradient thresholding is fast and has a low memory footprint. However, existing techniques that automate the selection of the gradient image threshold do not work across image modalities, multiple cell lines, and a wide range of foreground/background densities (requirement 2) and all failed the requirement for robust parameters that do not require re‐adjustment with time (requirement 5).

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

BackgroundThe 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.MethodsWe 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.ResultsThe 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.ConclusionsThe 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.

Correction of Location and Orientation Errors in Electromagnetic Motion Tracking

We describe a method for calibrating an electromagnetic motion tracking device. Algorithms for correcting both location and orientation data are presented. In particular, we use a method for interpolating rotation corrections that has not previously been used in this context. This method, unlike previous methods, is rooted in the geometry of the space of rotations. This interpolation method is used in conjunction with Delaunay tetrahedralization to enable correction based on scattered data samples. We present measurements that support the assumption that neither location nor orientation errors are dependent on sensor orientation. We give results showing large improvements in both location and orientation errors. The methods are shown to impose a minimal computational burden.