Mosaic vs. Single Image Analysis with Confocal Microscopy of the Corneal Nerve Plexus for Diagnosis of Early Diabetic Peripheral Neuropathy.

INTRODUCTION: 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. METHODS: 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/mm2), corneal nerve fibre density (CNFD; no./mm2), corneal nerve branch density (CNBD; no./mm2). Furthermore, we compared the data calculated with the novel mosaic, EG-based method with those received from single image analysis using different quantification tools. RESULTS: Using EG we did not find a significant difference between group 1 and group 2: CNFL (16.81 ± 5.87 mm/mm2 vs. 17.19 ± 7.19 mm/mm2, p = 0.895), CNFD (254.05 ± 115.36 no./mm2 vs. 265.91 ± 161.63 no./mm2, p = 0.732) and CNBD (102.68 ± 62.28 no./mm2 vs. 115.38 ± 96.91 no./mm2, p = 0.541). No significant difference between the EG method of analysing the SNP and the single image analysis of 10 images per patient was detected. CONCLUSION: On 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. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT05326958.

Assessment of dynamic corneal nerve changes using static landmarks by in vivo large-area confocal microscopy-a longitudinal proof-of-concept study.

Background: The purpose of the present proof-of-concept study was to use large-area in vivo confocal laser scanning microscopy (CLSM) mosaics to determine the migration rates of nerve branching points in the human corneal subbasal nerve plexus (SNP). Methods: Three healthy individuals were examined roughly weekly over a total period of six weeks by large-area in vivo confocal microscopy of the central cornea. An in-house developed prototype system for guided eye movement with an acquisition time of 40 s was used to image and generate large-area mosaics of the SNP. Kobayashi-structures and nerve entry points (EPs) were used as fixed structures to enable precise mosaic registration over time. The migration rate of 10 prominent nerve fiber branching points per participant was tracked and quantified over the longitudinal period. Results: Total investigation times of 10 minutes maximum per participant were used to generate mosaic images with an average size of 3.61 mm2 (range: 3.18-4.42 mm2). Overall mean branching point migration rates of (46.4±14.3), (48.8±15.5), and (50.9±13.9) µm/week were found for the three participants with no statistically significant difference. Longitudinal analyses of nerve branching point migration over time revealed significant time-dependent changes in migration rate only in participant 3 between the last two measurements [(63.7±12.3) and (43.0±12.5) µm/week, P<0.01]. Considering individual branching point dynamics, significant differences in nerve migration rate from the mean were only found in a few exceptions. Conclusions: The results of this proof-of-concept study have demonstrated the feasibility of using in vivo confocal microscopy to study the migration rates of corneal subbasal nerves within large areas of the central human cornea (>1 mm2). The ability to monitor dynamic changes in the SNP opens a window to future studies of corneal nerve health and regenerative capacity in a number of systemic and ocular diseases. Since corneal nerves are considered part of the peripheral nervous system, this technique could also offer an objective diagnostic tool and biomarker for disease- or treatment-induced neuropathic changes.

In Vivo Monitoring of Corneal Dendritic Cells in the Subbasal Nerve Plexus during Trastuzumab and Paclitaxel Breast Cancer Therapy-A One-Year Follow-Up.

Paclitaxel and trastuzumab have been associated with adverse effects including chemotherapy-induced peripheral neuropathy (CIPN) or ocular complications. In vivo confocal laser scanning microscopy (CLSM) of the cornea could be suitable for assessing side effects since the cornea is susceptible to, i.e., neurotoxic stimuli. The study represents a one-year follow-up of a breast cancer patient including large-area in vivo CLSM of the subbasal nerve plexus (SNP), nerve function testing, and questionnaires during paclitaxel and trastuzumab therapy. Six monitoring sessions (one baseline, four during, and one after therapy) over 58 weeks were carried out. Large-area mosaics of the SNP were generated, and identical regions within all sessions were assigned. While corneal nerve morphology did not cause alterations, the number of dendritic cells (DCs) showed dynamic changes with a local burst at 11 weeks after baseline. Simultaneously, paclitaxel treatment was terminated due to side effects, which, together with DCs, returned to normal levels as the therapy progressed. Longitudinal in vivo CLSM of the SNP could complement routine examinations and be helpful to generate a comprehensive clinical picture. The applied techniques, with corneal structures acting as biomarkers could represent a diagnostic tool for the objective assessment of the severity of adverse events and the outcome.

Real-time large-area imaging of the corneal subbasal nerve plexus.

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.

Wide-field mosaics of the corneal subbasal nerve plexus in Parkinson's disease using in vivo confocal microscopy.

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.

Burst of Corneal Dendritic Cells during Trastuzumab and Paclitaxel Treatment.

During breast cancer therapy, paclitaxel and trastuzumab are both associated with adverse effects such as chemotherapy-induced peripheral neuropathy and other systemic side effects including ocular complications. Corneal nerves are considered part of the peripheral nervous system and can be imaged non-invasively by confocal laser scanning microscopy (CLSM) on the cellular level. Thus, in vivo CLSM imaging of structures of the corneal subbasal nerve plexus (SNP) such as sensory nerves or dendritic cells (DCs) can be a powerful tool for the assessment of corneal complications during cancer treatment. During the present study, the SNP of a breast cancer patient was analyzed over time by using large-scale in vivo CLSM in the course of paclitaxel and trastuzumab therapy. The same corneal regions could be re-identified over time. While the subbasal nerve morphology did not alter significantly, a change in dendritic cell density and an additional local burst within the first 11 weeks of therapy was detected, indicating treatment-mediated corneal inflammatory processes. Ocular structures such as nerves and dendritic cells could represent useful biomarkers for the assessment of ocular adverse effects during cancer therapy and their management, leading to a better visual prognosis.

Parkinson's disease with restless legs syndrome-an in vivo corneal confocal microscopy study.

Small fiber neuropathy (SFN) has been suggested as a trigger of restless legs syndrome (RLS). An increased prevalence of peripheral neuropathy has been demonstrated in Parkinson's disease (PD). We aimed to investigate, in a cross-sectional manner, whether SFN is overrepresented in PD patients with concurrent RLS relative to PD patients without RLS, using in vivo corneal confocal microscopy (IVCCM) and quantitative sensory testing (QST) as part of small fiber assessment. Study participants comprised of age- and sex-matched PD patients with (n = 21) and without RLS (n = 21), and controls (n = 13). Diagnosis of RLS was consolidated with the sensory suggested immobilization test. Assessments included nerve conduction studies (NCS), Utah Early Neuropathy Scale (UENS), QST, and IVCCM, with automated determination of corneal nerve fiber length (CNFL) and branch density (CNBD) from wide-area mosaics of the subbasal nerve plexus. Plasma neurofilament light (p-NfL) was determined as a measure of axonal degeneration. No significant differences were found between groups when comparing CNFL (p = 0.81), CNBD (p = 0.92), NCS (p = 0.82), and QST (minimum p = 0.54). UENS scores, however, differed significantly (p = 0.001), with post-hoc pairwise testing revealing higher scores in both PD groups relative to controls (p = 0.018 and p = 0.001). Analysis of all PD patients (n = 42) revealed a correlation between the duration of L-dopa therapy and CNBD (ρ = -0.36, p = 0.022), and p-NfL correlated with UENS (ρ = 0.35, p = 0.026) and NCS (ρ = -0.51, p = 0.001). Small and large fiber neuropathy do not appear to be associated with RLS in PD. Whether peripheral small and/or large fiber pathology associates with central neurodegeneration in PD merits further longitudinal studies.

BeadNet: deep learning-based bead detection and counting in low-resolution microscopy images.

MOTIVATION: An automated counting of beads is required for many high-throughput experiments such as studying mimicked bacterial invasion processes. However, state-of-the-art algorithms under- or overestimate the number of beads in low-resolution images. In addition, expert knowledge is needed to adjust parameters. RESULTS: In combination with our image labeling tool, BeadNet enables biologists to easily annotate and process their data reducing the expertise required in many existing image analysis pipelines. BeadNet outperforms state-of-the-art-algorithms in terms of missing, added and total amount of beads. AVAILABILITY AND IMPLEMENTATION: BeadNet (software, code and dataset) is available at https://bitbucket.org/t_scherr/beadnet. The image labeling tool is available at https://bitbucket.org/abartschat/imagelabelingtool. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Segregation of Dispersed Silica Nanoparticles in Microfluidic Water-in-Oil Droplets: A Kinetic Study.

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.

Motion prediction enables simulated MR-imaging of freely moving model organisms.

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.

[Digital Image Processing and Deep Neural Networks in Ophthalmology - Current Trends]. / Digitale Bildverarbeitung und Tiefe Neuronale Netze in der Augenheilkunde ­ aktuelle Trends.

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.

Cellular in vivo 3D imaging of the cornea by confocal laser scanning microscopy.

We present an in vivo confocal laser scanning microscopy based method for large 3D reconstruction of the cornea on a cellular level with cropped volume sizes up to 266 x 286 x 396 µm3. The microscope objective used is equipped with a piezo actuator for automated, fast and precise closed-loop focal plane control. Furthermore, we present a novel concave surface contact cap, which significantly reduces eye movements by up to 87%, hence increasing the overlapping image area of the whole stack. This increases the cuboid volume of the generated 3D reconstruction significantly. The possibility to generate oblique sections using isotropic volume stacks opens the window to slit lamp microscopy on a cellular level.

3D confocal laser-scanning microscopy for large-area imaging of the corneal subbasal nerve plexus.

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.

EmbryoMiner: A new framework for interactive knowledge discovery in large-scale cell tracking data of developing embryos.

State-of-the-art light-sheet and confocal microscopes allow recording of entire embryos in 3D and over time (3D+t) for many hours. Fluorescently labeled structures can be segmented and tracked automatically in these terabyte-scale 3D+t images, resulting in thousands of cell migration trajectories that provide detailed insights to large-scale tissue reorganization at the cellular level. Here we present EmbryoMiner, a new interactive open-source framework suitable for in-depth analyses and comparisons of entire embryos, including an extensive set of trajectory features. Starting at the whole-embryo level, the framework can be used to iteratively focus on a region of interest within the embryo, to investigate and test specific trajectory-based hypotheses and to extract quantitative features from the isolated trajectories. Thus, the new framework provides a valuable new way to quantitatively compare corresponding anatomical regions in different embryos that were manually selected based on biological prior knowledge. As a proof of concept, we analyzed 3D+t light-sheet microscopy images of zebrafish embryos, showcasing potential user applications that can be performed using the new framework.

XPIWIT--an XML pipeline wrapper for the Insight Toolkit.

UNLABELLED: 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.

Fast segmentation of stained nuclei in terabyte-scale, time resolved 3D microscopy image stacks.

Automated analysis of multi-dimensional microscopy images has become an integral part of modern research in life science. Most available algorithms that provide sufficient segmentation quality, however, are infeasible for a large amount of data due to their high complexity. In this contribution we present a fast parallelized segmentation method that is especially suited for the extraction of stained nuclei from microscopy images, e.g., of developing zebrafish embryos. The idea is to transform the input image based on gradient and normal directions in the proximity of detected seed points such that it can be handled by straightforward global thresholding like Otsu's method. We evaluate the quality of the obtained segmentation results on a set of real and simulated benchmark images in 2D and 3D and show the algorithm's superior performance compared to other state-of-the-art algorithms. We achieve an up to ten-fold decrease in processing times, allowing us to process large data sets while still providing reasonable segmentation results.