Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Allgeier S

PROVIDER: S-EPMC5945773 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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

Allgeier Stephan S Bartschat Andreas A Bohn Sebastian S Peschel Sabine S Reichert Klaus-Martin KM Sperlich Karsten K Walckling Marcus M Hagenmeyer Veit V Mikut Ralf R Stachs Oliver O Köhler Bernd B

Scientific reports 20180510 1

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 ...[more]

PMID: 29749384

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Project description:INTRODUCTION:Laser scanning in vivo confocal microscopy (IVCM) enables non-invasive, high-resolution imaging of the cornea. In recent years, there has been a vast increase in researchers using laser scanning IVCM to image and quantify corneal nerve parameters. However, a range of methodological approaches have been adopted. The primary aim of this systematic review is to critically appraise the reported method(s) of primary research studies that have used laser scanning IVCM to quantify corneal sub-basal nerve plexus (SBNP) parameters in humans, and to examine corneal nerve parameters in healthy individuals. METHODS AND ANALYSIS:A systematic review of primary studies that have used laser scanning IVCM to quantify SBNP parameters in humans will be conducted. Comprehensive electronic searches will be performed in Ovid MedLine, Embase and the Cochrane Library. Two reviewers will independently assess titles and abstracts, and exclude studies not meeting the inclusion criteria. For studies judged eligible or potentially eligible, full texts will be independently assessed by two reviewers to determine eligibility. A third reviewer will resolve any discrepancies in judgement. Risk of bias will be assessed using a custom tool, covering five methodological domains: participant selection, method of image capture, method of image analysis, data reporting and other sources of bias. A systematic narrative synthesis of findings will be provided. A multilevel random-effects meta-analysis will be performed for corneal nerve parameters derived from healthy participants. This review will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. ETHICS AND DISSEMINATION:As this review considers published data, ethical approval is not required. We foresee that this synthesis will serve as a reference for future studies, and can be used to inform best practice standards for using IVCM in clinical research. A manuscript reporting the results of the review will be published and may also be presented at scientific conferences.

Project description:PurposeIn vivo confocal microscopy (IVCM) is a noninvasive, reproducible, and inexpensive diagnostic tool for corneal diseases. However, widespread and effortless image acquisition in IVCM creates serious image analysis workloads on ophthalmologists, and neural networks could solve this problem quickly. We have produced a novel deep learning algorithm based on generative adversarial networks (GANs), and we compare its accuracy for automatic segmentation of subbasal nerves in IVCM images with a fully convolutional neural network (U-Net) based method.MethodsWe have collected IVCM images from 85 subjects. U-Net and GAN-based image segmentation methods were trained and tested under the supervision of three clinicians for the segmentation of corneal subbasal nerves. Nerve segmentation results for GAN and U-Net-based methods were compared with the clinicians by using Pearson's R correlation, Bland-Altman analysis, and receiver operating characteristics (ROC) statistics. Additionally, different noises were applied on IVCM images to evaluate the performances of the algorithms with noises of biomedical imaging.ResultsThe GAN-based algorithm demonstrated similar correlation and Bland-Altman analysis results with U-Net. The GAN-based method showed significantly higher accuracy compared to U-Net in ROC curves. Additionally, the performance of the U-Net deteriorated significantly with different noises, especially in speckle noise, compared to GAN.ConclusionsThis study is the first application of GAN-based algorithms on IVCM images. The GAN-based algorithms demonstrated higher accuracy than U-Net for automatic corneal nerve segmentation in IVCM images, in patient-acquired images and noise applied images. This GAN-based segmentation method can be used as a facilitating diagnostic tool in ophthalmology clinics.Translational relevanceGenerative adversarial networks are emerging deep learning models for medical image processing, which could be important clinical tools for rapid segmentation and analysis of corneal subbasal nerves in IVCM images.

Dataset Information

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

Publications

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

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