Dadong Wang

Automated analysis of neurite branching in cultured cortical neurons using HCA-Vision

Manual neuron tracing is a very labor‐intensive task. In the drug screening context, the sheer number of images to process means that this approach is unrealistic. Moreover, the lack of reproducibility, objectivity, and auditing capability of manual tracing is limiting even in the context of smaller studies. We have developed fast, sensitive, and reliable algorithms for the purpose of detecting and analyzing neurites in cell cultures, and we have integrated them in software called HCA‐Vision, suitable for the research environment. We validate the software on images of cortical neurons by comparing results obtained using HCA‐Vision with those obtained using an established semi‐automated tracing solution (NeuronJ). The effect of the Sez‐6 deletion was characterized in detail. Sez‐6 null neurons exhibited a significant increase in neurite branching, although the neurite field area was unchanged due to a reduction in mean branch length. HCA‐Vision delivered considerable speed benefits and reliable traces.

HCA-Vision Automated Neurite Outgrowth Analysis

Automating the analysis of neurons in culture represents a key aspect of the search for neuroactive compounds. A number of commercial neurite analysis software packages tend to measure some basic features such as total neurite length and number of branching points. However, with only these measurements, some differences between neurite morphologies that are clear to a human observer cannot be identified. The authors have developed a suite of image analysis tools that will allow researchers to produce quality analyses at primary screening rates. The suite provides sensitive and information-rich measurements of neurons and neurites. It can discriminate subtle changes in complex neurite arborization even when neurons and neurites are dense. This allows users to selectively screen for compounds triggering different types of neurite outgrowth behavior. In mixed cell populations, neurons can be filtered and separated from other brain cell types so that neurite analysis can be performed only on neurons. It supports batch processing with a built-in database to store the batch-processing results, a batch result viewer, and an ad hoc query builder for users to retrieve features of interest. The suite of tools has been deployed into a software package called HCA-Vision. The free version of the software package is available at http://www.hca-vision.com.

Segmentation and tracking individual Pseudomonas aeruginosa bacteria in dense populations of motile cells

The dynamics of individual bacteria underlies the manifestation of complex multicellular behaviours such as biofilm development and colony expansion. High resolution movies of expanding bacterial colonies reveal intriguing patterns of cell motions. A quantitative understanding of the observed behaviour in relation to the bacterias own motile apparatus and to hydrodynamic forces requires that bacteria be identified and tracked over time. This represents a demanding undertaking as their size is close to the diffraction limit; they are very close to each other; and a typical image may contain over a thousand cells. Here, we describe the approach that we have developed to segment individual bacteria and track them in high resolution phase contrast microscopy movies. We report that over 99% of non-overlapping bacteria could be segmented correctly using mathematical morphology, and we present preliminary results that exploit this new capability.

Neuroprotective effects of apigenin against inflammation, neuronal excitability and apoptosis in an induced pluripotent stem cell model of Alzheimer’s disease

Alzheimer’s disease (AD) is one of the most prevalent neurodegenerative diseases, yet current therapeutic treatments are inadequate due to a complex disease pathogenesis. The plant polyphenol apigenin has been shown to have anti-inflammatory and neuroprotective properties in a number of cell and animal models; however a comprehensive assessment has not been performed in a human model of AD. Here we have used a human induced pluripotent stem cell (iPSC) model of familial and sporadic AD, in addition to healthy controls, to assess the neuroprotective activity of apigenin. The iPSC-derived AD neurons demonstrated a hyper-excitable calcium signalling phenotype, elevated levels of nitrite, increased cytotoxicity and apoptosis, reduced neurite length and increased susceptibility to inflammatory stress challenge from activated murine microglia, in comparison to control neurons. We identified that apigenin has potent anti-inflammatory properties with the ability to protect neurites and cell viability by promoting a global down-regulation of cytokine and nitric oxide (NO) release in inflammatory cells. In addition, we show that apigenin is able to protect iPSC-derived AD neurons via multiple means by reducing the frequency of spontaneous Ca2+ signals and significantly reducing caspase-3/7 mediated apoptosis. These data demonstrate the broad neuroprotective action of apigenin against AD pathogenesis in a human disease model.

Membrane boundary extraction using circular multiple paths

Membrane proteins represent over 50% of known drug targets. Accordingly, several widely used assays in the high content analysis area rely on quantitative measures of the translocation of proteins between intracellular organelles and the cell surface. In order to increase the sensitivity of these assays, one needs to measure the signal specifically along the membrane, requiring a precise segmentation of this compartment. Manual tracing of membrane boundary is very time-consuming and confronts us with issues of objectivity and reproducibility. In this paper, we present an approach based on a circular multiple paths technique on transformed images that enables us to segment the membrane compartment accurately and rapidly. We have presented three approaches for image transformation. The circular property of the multiple paths ensures that we are obtaining closed contours for the membrane boundary. The position of the multiple paths provides the edges of the membrane boundary. The effectiveness of our algorithm is illustrated using cells expressing epitope-tagged membrane proteins.

Linear Feature Detection on GPUs

The acceleration of an existing linear feature detection algorithm for 2D images using GPUs is discussed. The two most time consuming components of this process are implemented on the GPU, namely, linear feature detection using dual-peak directional non-maximum suppression, and a gap filling process that joins disconnected feature masks to rectify false negatives. Multiple steps or image filters in each component are combined into a single GPU kernel to minimise data transfers to off-chip GPU RAM, and issues relating to on-chip memory utilisation, caching, and memory coalescing are considered. The presented algorithm is useful for applications needing to analyse complex linear structures, and examples are given for dense neurite images from the biotech domain.

Galaxy + Hadoop: Toward a Collaborative and Scalable Image Processing Toolbox in Cloud

With emergence and adoption of cloud computing, cloud has become an effective collaboration platform for integrating various software tools to deliver as services. In this paper, we present a cloud-based image processing toolbox by integrating Galaxy, Hadoop and our proprietary image processing tools. This toolbox allows users to easily design and execute complex image processing tasks by sharing various advanced image processing tools and scalable cloud computation capacity. The paper provides the integration architecture and technical details about the whole system. In particular, we present our investigations to use Hadoop to handle massive image processing jobs in the system. A number of real image processing examples are used to demonstrate the usefulness and scalability of this class of data-intensive applications.

Automated Opal Grading by Imaging and Statistical Learning

Quantitative grading of opals is a challenging task even for skilled opal assessors. Current opal evaluation practices are highly subjective due to the complexities of opal assessment and the limitations of human visual observation. In this paper, we present a novel machine vision system for the automated grading of opals-the gemological digital analyzer (GDA). The grading is based on statistical machine learning with multiple characteristics extracted from opal images. The assessment workflow includes calibration, opal image capture, image analysis, and opal classification and grading. Experimental results show that the GDA-based grading is more consistent and objective compared with the manual evaluations conducted by the skilled opal assessors.

Applications of heterogeneous computing in computational and simulation science

As the size and complexity of scientific problems and datasets grow, scientists from a broad range of discipline areas are relying more and more on computational methods and simulations to help solve their problems. This paper presents a summary of heterogeneous algorithms and applications that have been developed by a large research organization (CSIRO) for solving practical and challenging science problems faster than is possible with conventional multi-core CPUs alone. The problem domains discussed include biological image analysis, computed tomography reconstruction, marine biogeochemical models, fluid dynamics, and bioinformatics. The algorithms utilize GPUs and multi-core CPUs on a scale ranging from single workstation installations through to large GPU clusters. Results demonstrate that large GPU clusters can be used to accelerate a variety of practical science applications, and justify the significant financial investment and interest being placed into such systems.

A robust method for high-precision quantification of the complex three-dimensional vasculatures acquired by X-ray microtomography.

The quantification of micro-vasculatures is important for the analysis of angiogenesis on which the detection of tumor growth or hepatic fibrosis depends. Synchrotron-based X-ray computed micro-tomography (SR-µCT) allows rapid acquisition of micro-vasculature images at micrometer-scale spatial resolution. Through skeletonization, the statistical features of the micro-vasculature can be extracted from the skeleton of the micro-vasculatures. Thinning is a widely used algorithm to produce the vascular skeleton in medical research. Existing three-dimensional thinning methods normally emphasize the preservation of topological structure rather than geometrical features in generating the skeleton of a volumetric object. This results in three problems and limits the accuracy of the quantitative results related to the geometrical structure of the vasculature. The problems include the excessively shortened length of elongated objects, eliminated branches of blood vessel tree structure, and numerous noisy spurious branches. The inaccuracy of the skeleton directly introduces errors in the quantitative analysis, especially on the parameters concerning the vascular length and the counts of vessel segments and branching points. In this paper, a robust method using a consolidated end-point constraint for thinning, which generates geometry-preserving skeletons in addition to maintaining the topology of the vasculature, is presented. The improved skeleton can be used to produce more accurate quantitative results. Experimental results from high-resolution SR-µCT images show that the end-point constraint produced by the proposed method can significantly improve the accuracy of the skeleton obtained using the existing ITK three-dimensional thinning filter. The produced skeleton has laid the groundwork for accurate quantification of the angiogenesis. This is critical for the early detection of tumors and assessing anti-angiogenesis treatments.