Search

The assessment of the corneal nerve fibre plexus with corneal confocal microscopy (CCM) is an upcoming but still experimental method in the diagnosis of early stage diabetic peripheral neuropathy (DPN). Using an innovative imaging technique-Heidelberg Retina Tomograph equipped with the Rostock Cornea Module (HRT-RCM) and EyeGuidance module (EG)-we were able to look at greater areas of subbasal nerve plexus (SNP) in order to increase the diagnostic accuracy. The aim of our study was to evaluate the usefulness of EG instead of single image analysis in diagnosis of early stage DPN.This prospective study was performed on 60 patients with type 2 diabetes mellitus, classified equally into two subgroups based on neuropathy deficient score (NDS): patients without DPN (group 1) or with mild DPN (group 2). The following parameters were analysed in the two subgroups: corneal nerve fibre length (CNFL; mm/mmUsing EG we did not find a significant difference between group 1 and group 2: CNFL (16.81 ± 5.87 mm/mmOn the basis of our results it was not possible to differentiate between early stages of large nerve fibre DPN in patients with type 2 diabetes mellitus via SNP analysis. To improve sensitivity and specificity of this method newer technologies are under current evaluation.ClinicalTrials.gov Identifier NCT05326958.

Dispersed negatively charged silica nanoparticles segregate inside microfluidic water‐in‐oil (W/O) droplets that are coated with a positively charged lipid shell. We report a methodology for the quantitative analysis of this self‐assembly process. By using real‐time fluorescence microscopy and automated analysis of the recorded images, kinetic data are obtained that characterize the electrostatically‐driven self‐assembly. We demonstrate that the segregation rates can be controlled by the installment of functional moieties on the nanoparticle's surface, such as nucleic acid and protein molecules. We anticipate that our method enables the quantitative and systematic investigation of the segregation of (bio)functionalized nanoparticles in microfluidic droplets. This could lead to complex supramolecular architectures on the inner surface of micrometer‐sized hollow spheres, which might be used, for example, as cell containers for applications in the life sciences., Studying segregation: The segregation behavior of functionalized silica nanoparticles in charged microfluidic droplets was quantitatively analyzed using a home‐made microfluidic device with automated data processing. Such self‐assembly systems can be exploited for life science applications.

The purpose of the present proof-of-concept study was to use large-areaThree healthy individuals were examined roughly weekly over a total period of six weeks by large-areaTotal investigation times of 10 minutes maximum per participant were used to generate mosaic images with an average size of 3.61 mmThe results of this proof-of-concept study have demonstrated the feasibility of using

The morphometric assessment of the corneal subbasal nerve plexus (SNP) by confocal microscopy holds great potential as a sensitive biomarker for various ocular and systemic conditions and diseases. Automated wide-field montages (or large-area mosaic images) of the SNP provide an opportunity to overcome the limited field of view of the available imaging systems without the need for manual, subjective image selection for morphometric characterization. However, current wide-field montaging solutions usually calculate the mosaic image after the examination session, without a reliable means for the clinician to predict or estimate the resulting mosaic image quality during the examination. This contribution describes a novel approach for a real-time creation and visualization of a mosaic image of the SNP that facilitates an informed evaluation of the quality of the acquired image data immediately at the time of recording. In cases of insufficient data quality, the examination can be aborted and repeated immediately, while the patient is still at the microscope. Online mosaicking also offers the chance to identify an overlap of the imaged tissue region with previous SNP mosaic images, which can be particularly advantageous for follow-up examinations.

ZusammenfassungDer Einsatz von Tiefen Neuronalen Netzen (Deep Learning) eröffnet neue Möglichkeiten in der digitalen Bildverarbeitung. Auch für die Auswertung von Bilddaten in der Ophthalmologie wird diese Methode erfolgreich eingesetzt und findet weite Verbreitung. In diesem Artikel wird die methodische Vorgehensweise beim Deep Learning betrachtet und der klassischen Vorgehensweise für die Entwicklung von Methoden für die digitale Bildverarbeitung gegenübergestellt. Dabei wird auf Unterschiede eingegangen und die wichtiger werdende Rolle von Trainingsdaten für die Modellbildung erklärt. Weiterhin wird die Vorgehensweise des Transfer-Lernens (Transfer Learning) für Deep Learning am Beispiel eines Datensatzes aus der kornealen Konfokalmikroskopie vorgestellt. Dabei wird auf die Vorteile der Methode und auf Besonderheiten beim Umgang mit medizinischen Mikroskopdaten eingegangen.

In vivo confocal microscopy (IVCM) is a non-invasive imaging technique facilitating real-time acquisition of images from the live cornea and its layers with high resolution (1–2 µm) and high magnification (600 to 800-fold). IVCM is extensively used to examine the cornea at a cellular level, including the subbasal nerve plexus (SBNP). IVCM of the cornea has thus gained intense interest for probing ophthalmic and systemic diseases affecting peripheral nerves. One of the main drawbacks, however, is the small field of view of IVCM, preventing an overview of SBNP architecture and necessitating subjective image sampling of small areas of the SBNP for analysis. Here, we provide a high-quality dataset of the corneal SBNP reconstructed by automated mosaicking, with an average mosaic image size corresponding to 48 individual IVCM fields of view. The mosaic dataset represents a group of 42 individuals with Parkinson’s disease (PD) with and without concurrent restless leg syndrome. Additionally, mosaics from a control group (n = 13) without PD are also provided, along with clinical data for all included participants., Measurement(s)Prominent corneal nerve fibers • Dendritic CellTechnology Type(s)confocal microscopyFactor Type(s)disease status • comorbiditySample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16691452

Organogels (hydrophobic polymer gels) are soft materials based on polymeric networks swollen in organic solvents. They are hydrophobic and possess a high content of solvent and low surface adhesion, rendering them interesting in applications such as encapsulants, drug delivery, actuators, slippery surfaces (self-cleaning, anti-waxing, anti-bacterial), or for oil-water separation. To design functional organogels, strategies to control their shape and surface structure are required. Herein, the inherent UV photodegradability of poly(methacrylate) organogels is reported. No additional photosensitizers are required to efficiently degrade organogels (d ≈ 1 mm) on the minute scale. A low UV absorbance and a high swelling ability of the solvent infusing the organogel are found to be beneficial for fast photodegradation, which is expected to be transferrable to other gel photochemistry. Organogel arrays, films, and structured organogel surfaces are prepared, and their extraction ability and slippery properties are examined. Films of inherently photodegradable organogels on copper circuit boards serve as the first ever positive gel photoresist. Spatially photodegraded organogel films protect or reveal copper surfaces against an etchant (FeCl3 aq.).

Bioinformatics 36(17), 4668-4670 (2020). doi:10.1093/bioinformatics/btaa594, Published by Oxford Univ. Press, Oxford

Der subbasale Nervenplexus (SNP) der Kornea bietet die Moglichkeit, periphere Nervenstrukturen nichtinvasiv in vivo mithilfe der Konfokalmikroskopie (CCM) zu untersuchen. Morphologische Veranderungen des SNP konnen so unmittelbar detektiert und quantitativ charakterisiert werden. Aufgrund der inhomogenen Verteilung der Nervenfasern reicht eine einzelne CCM-Aufnahme fur eine valide Diagnose nicht aus. Gesucht werden daher Verfahren zur grosflachigen Erfassung des SNP. Dieser Beitrag gibt einen Uberblick uber publizierte Ansatze zur Losung dieses Problems. Aktuelle Entwicklungsarbeiten am Karlsruher Institut fur Technologie und der Universitatsaugenklinik in Rostock lassen zukunftig eine vereinfachte Handhabung der Technologie und eine weitere Verbesserung der Bildqualitat erwarten.

The use of deep neural networks ("deep learning") creates new possibilities in digital image processing. This approach has been widely applied and successfully used for the evaluation of image data in ophthalmology. In this article, the methodological approach of deep learning is examined and compared to the classical approach for digital image processing. The differences between the approaches are discussed and the increasingly important role of training data for model generation is explained. Furthermore, the approach of transfer learning for deep learning is presented with a representative data set from the field of corneal confocal microscopy. In this context, the advantages of the method and the specific problems when dealing with medical microscope data will be discussed.Der Einsatz von Tiefen Neuronalen Netzen (Deep Learning) eröffnet neue Möglichkeiten in der digitalen Bildverarbeitung. Auch für die Auswertung von Bilddaten in der Ophthalmologie wird diese Methode erfolgreich eingesetzt und findet weite Verbreitung. In diesem Artikel wird die methodische Vorgehensweise beim Deep Learning betrachtet und der klassischen Vorgehensweise für die Entwicklung von Methoden für die digitale Bildverarbeitung gegenübergestellt. Dabei wird auf Unterschiede eingegangen und die wichtiger werdende Rolle von Trainingsdaten für die Modellbildung erklärt. Weiterhin wird die Vorgehensweise des Transfer-Lernens (Transfer Learning) für Deep Learning am Beispiel eines Datensatzes aus der kornealen Konfokalmikroskopie vorgestellt. Dabei wird auf die Vorteile der Methode und auf Besonderheiten beim Umgang mit medizinischen Mikroskopdaten eingegangen.

Magnetic resonance tomography typically applies the Fourier transform to k-space signals repeatedly acquired from a frequency encoded spatial region of interest, therefore requiring a stationary object during scanning. Any movement of the object results in phase errors in the recorded signal, leading to deformed images, phantoms, and artifacts, since the encoded information does not originate from the intended region of the object. However, if the type and magnitude of movement is known instantaneously, the scanner or the reconstruction algorithm could be adjusted to compensate for the movement, directly allowing high quality imaging with non-stationary objects. This would be an enormous boon to studies that tie cell metabolomics to spontaneous organism behaviour, eliminating the stress otherwise necessitated by restraining measures such as anesthesia or clamping. In the present theoretical study, we use a phantom of the animal model C. elegans to examine the feasibility to automatically predict its movement and position, and to evaluate the impact of movement prediction, within a sufficiently long time horizon, on image reconstruction. For this purpose, we use automated image processing to annotate body parts in freely moving C. elegans, and predict their path of movement. We further introduce an MRI simulation platform based on bright field videos of the moving worm, combined with a stack of high resolution transmission electron microscope (TEM) slice images as virtual high resolution phantoms. A phantom provides an indication of the spatial distribution of signal-generating nuclei on a particular imaging slice. We show that adjustment of the scanning to the predicted movements strongly reduces distortions in the resulting image, opening the door for implementation in a high-resolution NMR scanner., PLoS Computational Biology, 15 (12), ISSN:1553-734X, ISSN:1553-7358

Corneal confocal laser scanning microscopy is a promising method for in vivo investigation of cellular structures, e. g., of nerve fibers in the sub-basal nerve plexus. During recording, even slight displacements of the focal plane lead to images of adjacent tissue layers. In this work, we propose a closed-loop control of the focal plane. To detect and evaluate the visible tissues, we utilize the Bag of Visual Words approach to implement a customizable image processing pipeline for real-time applications. Furthermore, we show that the proposed model can be trained with small classification datasets and can be applied as a segmentation method. The proposed control loop, including tissue detection, is implemented in a proof-of-concept setup and shows promising results in a first evaluation with a human subject.

The capability of corneal confocal microscopy (CCM) to acquire high-resolution in vivo images of the densely innervated human cornea has gained considerable interest in using this non-invasive technique as an objective diagnostic tool for staging peripheral neuropathies. Morphological alterations of the corneal subbasal nerve plexus (SNP) assessed by CCM have been shown to correlate well with the progression of neuropathic diseases and even predict future-incident neuropathy. Since the field of view of single CCM images is insufficient for reliable characterisation of nerve morphology, several image mosaicking techniques have been developed to facilitate the assessment of the SNP in large-area visualisations. Due to the limited depth of field of confocal microscopy, these approaches are highly sensitive to small deviations of the focus plane from the SNP layer. Our contribution proposes a new automated solution, combining guided eye movements for rapid expansion of the acquired SNP area and axial focus plane oscillations to guarantee complete imaging of the SNP. We present results of a feasibility study using the proposed setup to evaluate different oscillation settings. By comparing different image selection approaches, we show that automatic tissue classification algorithms are essential to create high-quality mosaic images from the acquired 3D datasets.

Automated segmentation approaches are crucial to quantitatively analyze large-scale 3D microscopy images. Particularly in deep tissue regions, automatic methods still fail to provide error-free segmentations. To improve the segmentation quality throughout imaged samples, we present a new supervoxel-based 3D segmentation approach that outperforms current methods and reduces the manual correction effort. The algorithm consists of gentle preprocessing and a conservative super-voxel generation method followed by supervoxel agglomeration based on local signal properties and a postprocessing step to fix under-segmentation errors using a Convolutional Neural Network. We validate the functionality of the algorithm on manually labeled 3D confocal images of the plant Arabidopis thaliana and compare the results to a state-of-the-art meristem segmentation algorithm., Comment: 5 pages, 3 figures, 1 table

Summary: The Insight Toolkit offers plenty of features for multidimensional image analysis. Current implementations, however, often suffer either from a lack of flexibility due to hard-coded C++ pipelines for a certain task or by slow execution times, e.g. caused by inefficient implementations or multiple read/write operations for separate filter execution. We present an XML-based wrapper application for the Insight Toolkit that combines the performance of a pure C++ implementation with an easy-to-use graphical setup of dynamic image analysis pipelines. Created XML pipelines can be interpreted and executed by XPIWIT in console mode either locally or on large clusters. We successfully applied the software tool for the automated analysis of terabyte-scale, time-resolved 3D image data of zebrafish embryos. Availability and implementation: XPIWIT is implemented in C++ using the Insight Toolkit and the Qt SDK. It has been successfully compiled and tested under Windows and Unix-based systems. Software and documentation are distributed under Apache 2.0 license and are publicly available for download at https://bitbucket.org/jstegmaier/xpiwit/downloads/. Contact: johannes.stegmaier@kit.edu Supplementary information: Supplementary data are available at Bioinformatics online.