Derek Juba

Parallel, stochastic measurement of molecular surface area.

Biochemists often wish to compute surface areas of proteins. A variety of algorithms have been developed for this task, but they are designed for traditional single-processor architectures. The current trend in computer hardware is towards increasingly parallel architectures for which these algorithms are not well suited. We describe a parallel, stochastic algorithm for molecular surface area computation that maps well to the emerging multi-core architectures. Our algorithm is also progressive, providing a rough estimate of surface area immediately and refining this estimate as time goes on. Furthermore, the algorithm generates points on the molecular surface which can be used for point-based rendering. We demonstrate a GPU implementation of our algorithm and show that it compares favorably with several existing molecular surface computation programs, giving fast estimates of the molecular surface area with good accuracy.

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.

Using Graphics Processors for High-Performance Computation and Visualization of Plasma Turbulence

Direct numerical simulation (DNS) of turbulence is computationally intensive and typically relies on some form of parallel processing. The authors present techniques to map DNS computations to modern graphics processing units (GPUs), which are characterized by very high memory bandwidth and hundreds of SPMD (single-program-multiple-data) processors.

A method for the evaluation of thousands of automated 3D stem cell segmentations

There is no segmentation method that performs perfectly with any dataset in comparison to human segmentation. Evaluation procedures for segmentation algorithms become critical for their selection. The problems associated with segmentation performance evaluations and visual verification of segmentation results are exaggerated when dealing with thousands of three‐dimensional (3D) image volumes because of the amount of computation and manual inputs needed.

3D Cellular Morphotyping of Scaffold Niches

There is currently no method for assessing the nature of the cell niche provided by 3D biomaterial scaffolds. Analyzing human bone marrow stromal cell (hBMSC) 3D cell shape in response to different biomaterial scaffolds allowed the 3D cell niche promoted by biomaterial scaffolds to be evaluated. Primary hBMSCs (p5) were seeded (5,000 cells/cm2) in 10 different biomaterial scaffolds and cultured for 24 h. Samples were fixed and stained for actin and nucleus, imaged with confocal microscopy to obtain a 3D volume (z-stack), and 3D cell shape was analyzed with computational approaches. Over 100 cells were imaged per scaffold group (10 scaffold groups, ~1250 cells total), resulting in the largest known 3D stem cell dataset (~135,000 files, ~135 GB) and enabling a high degree of statistical rigor. The images were segmented using an automated algorithm and a final dataset of 969 well-segmented cells were analyzed with 79 shape metrics, which enabled 3D cellular morphotyping of scaffold niches. The variety of scaffolds studied promoted different cell morphologies during culture and there were significant differences in shape metrics, particularly for cell depth, surface area, and volume. This study demonstrated a quantitative approach to analyze 3D cell shape and morphotype and is the largest known study analyzing 3D cell shape in response to a variety of biomaterial scaffolds. The dataset is publically accessible with an online 3D viewer. These results could inform the selection of prospective scaffolds for applications based on 3D cell shape in the tissue of interest.

Confluent Volumetric Visualization of Gyrokinetic Turbulence

Data from gyrokinetic turbulence codes are often difficult to visualize due their high dimensionality, the nontrivial geometry of the underlying grids, and the vast range of spatial scales. We present an interactive visualization framework that attempts to address these issues. Images from a nonlinear gyrokinetic simulation are presented.