Background: Currently, imaging technologies that can accurately assess or provide surrogate markers of the human cutaneous microvessel network are limited. Dynamic optical coherence tomography (D-OCT) allows the detec...
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Background: Currently, imaging technologies that can accurately assess or provide surrogate markers of the human cutaneous microvessel network are limited. Dynamic optical coherence tomography (D-OCT) allows the detection of blood flow in vivo and visualization of the skin microvasculature. However, imageprocessing is necessary to correct images, filter artifacts, and exclude irrelevant signals. The objective of this study was to develop a novel image processing workflow to enhance the technical capabilities of D-OCT. Materials and methods: Single-center, vehicle-controlled study including healthy vol- unteers aged 18-50 years. A capsaicin solution was applied topically on the subject's forearm to induce local inflammation. Measurements of capsaicin-induced increase in dermal blood flow, within the region of interest, were performed by laser Doppler imaging (LDI) (reference method) and D-OCT. Results: Sixteen subjects were enrolled. A good correlation was shown between D-OCT and LDI, using the image processing workflow. Therefore, D-OCT offers an easy-to-use alternative to LDI, with good repeatability, new robust morphological features (dermal-epidermal junction localization), and quantification of the distribution of vessel size and changes in this distribution induced by capsaicin. The visualization of the vessel network was improved through bloc filtering arid artifact removal. Moreover, the assessment of vessel size distribution allows a fine analysis of the vascular patterns. Conclusion: The newly developed image processing workflow enhances the technical capabilities of D-OCT for the accurate detection and characterization of microcirculation in the skin. A direct clinical application of this image processing workflow is the quantification of the effect of topical treatment on skin vascularization.
Computer Vision (CV) leverages artificial intelligence to analyse digital images, offering insights for a wide range of different applications. While CV software often relies on open-source libraries such as OpenCV, i...
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Computer Vision (CV) leverages artificial intelligence to analyse digital images, offering insights for a wide range of different applications. While CV software often relies on open-source libraries such as OpenCV, it is probably more common for this software to use custom codes. Creating particular solutions stems from the very nature of the specific CV problems being addressed but, despite these particularities, there are common links at the core that are either not addressed by generic CV libraries or require significant customisation for specific applications. Understanding the nature of real problems faced by a digital image analysis use case can contribute as much as solving a generic CV problem, and this is the aim of this paper. This article addresses the problem of migrating to CUDA a part of Multiscan Vision System, a complex CV workflow utilised in a real-world, industrial use case. The primary challenge lies in minimising the overhead due to data transfers between the host and GPU (graphics processing unit), or even within the device's memory itself. While the speed-up achieved may not rival that of other applications more suitable to GPU architecture (in particular, massively data parallel applications), the algorithms and data distribution proposed in this study effectively offload a substantial portion of the workflow to the GPU in the context of low (integer) arithmetic intensity and real-time constraints. This frees the CPU to handle other workflow components and increases the capability to incorporate more cameras, significantly boosting productivity and economic performance.
Structural biologists have traditionally approached cellular complexity in a reductionist manner in which the cellular molecular components are fractionated and purified before being studied individually. This 'di...
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Structural biologists have traditionally approached cellular complexity in a reductionist manner in which the cellular molecular components are fractionated and purified before being studied individually. This 'divide and conquer' approach has been highly successful. However, awareness has grown in recent years that biological functions can rarely be attributed to individual macromolecules. Most cellular functions arise from their concerted action, and there is thus a need for methods enabling structural studies performed in situ, ideally in unperturbed cellular environments. Cryo-electron tomography (Cryo-ET) combines the power of 3D molecular-level imaging with the best structural preservation that is physically possible to achieve. Thus, it has a unique potential to reveal the supramolecular architecture or 'molecular sociology' of cells and to discover the unexpected. Here, we review state-of-the-art Cryo-ET workflows, provide examples of biological applications, and discuss what is needed to realize the full potential of Cryo-ET.
We have developed a software package towards automatic electron tomography (ET): Automatic Tomography (AuTom). The presented package has the following characteristics: accurate alignment modules for marker-free datase...
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We have developed a software package towards automatic electron tomography (ET): Automatic Tomography (AuTom). The presented package has the following characteristics: accurate alignment modules for marker-free datasets containing substantial biological structures;fully automatic alignment modules for datasets with fiducial markers;wide coverage of reconstruction methods including a new iterative method based on the compressed-sensing theory that suppresses the "missing wedge" effect;and multi-platform acceleration solutions that support faster iterative algebraic reconstruction. AuTom aims to achieve fully automatic alignment and reconstruction for electron tomography and has already been successful for a variety of datasets. AuTom also offers user-friendly interface and auxiliary designs for file management and workflow management, in which fiducial marker-based datasets and marker-free datasets are addressed with totally different subprocesses. With all of these features, AuTom can serve as a convenient and effective tool for processing in electron tomography.
The Bsoft package [Heymann, J.B., Belnap, D.M., 2007. Bsoft: imageprocessing and molecular modeling for electron microscopy. J. Struct. Biol. 157, 3-18] has been enhanced by adding utilities for processing electron t...
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The Bsoft package [Heymann, J.B., Belnap, D.M., 2007. Bsoft: imageprocessing and molecular modeling for electron microscopy. J. Struct. Biol. 157, 3-18] has been enhanced by adding utilities for processing electron tomographic (ET) data;in particular, cryo-ET data characterized by low contrast and high noise. To handle the high computational load efficiently, a workflow was developed, based on the database-like parameter handling in Bsoft, aimed at minimizing user interaction and facilitating automation. To the same end, scripting elements distribute the processing among multiple processors on the same or different computers. The resolution of a tomogram depends on the precision of projection alignment, which is usually based on pinpointing fiducial markers (electron-dense gold particles). Alignment requires accurate specification of the tilt axis, and our protocol includes a procedure for determining it to adequate accuracy. Refinement of projection alignment provides information that allows assessment of its precision, as well as projection quality control. We implemented a reciprocal space algorithm that affords an alternative to back-projection or real space algorithms for calculating tomograms. Resources are also included that allow resolution assessment by cross-validation (NLOO2D);denoising and interpretation;and the extraction, mutual alignment, and averaging of tomographic sub-volumes. Published by Elsevier Inc.
Bsoft is a software package written for imageprocessing of electron micrographs, interpretation of reconstructions, molecular modeling, and general imageprocessing. The code is modularized to allow for rapid testing...
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Bsoft is a software package written for imageprocessing of electron micrographs, interpretation of reconstructions, molecular modeling, and general imageprocessing. The code is modularized to allow for rapid testing and deployment of new processing algorithms, while also providing sufficient infrastructure to deal with many file formats and parametric data. The design is deliberately open to allow interchange of information with other image and Molecular processing software through a standard parameter file (Currently a text-based encoding Of parameters in the STAR Format) and its support of multiple image and molecular formats. It also allows shell scripting of processes and allows subtasks to be distributed across multiple computers for Concurrent processing. Bsoft has undergone many modifications and advancements since its initial release [Heymann, J.B., 2001. Bsoft: image and Molecular processing in electron microscopy. J. StrUCt. Biol. 133, 156-169]. Much of the emphasis is oil single particle analysis and tomography, and sufficient functionality is available in the package to support most needed operations for these techniques. The key graphical user interface is the program bshow, which displays an image and is used for many interactive purposes Such as fitting the contrast transfer function or picking particles. Bsoft also offers various tools to manipulate atomic structures and to refine the fit of a known molecular structure to a density in a reconstruction. Published by Elsevier Inc.
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