Non-invasive high-contrast imaging of conjunctival goblet cells for the precision diagnosis of ocular surface diseases

Imaging assessment of conjunctival goblet cells in dry eye disease

Dry eye disease (DED) is a widespread, multifactorial, and chronic disorder of the ocular surface with disruption of tear film homeostasis as its core trait. Conjunctival goblet cells (CGCs) are specialised secretory cells found in the conjunctival epithelium that participate in tear film formation by secreting mucin. Changes in both the structure and function of CGCs are hallmarks of DED, and imaging assessment of CGCs is important for the diagnosis, classification, and severity evaluation of DED. Existing imaging methods include conjunctival biopsy, conjunctival impression cytology and in vivo confocal microscopy, which can be used to assess the morphology, distribution, and density of the CGCs. Recently, moxifloxacin-based fluorescence microscopy has emerged as a novel technique that enables efficient, non-invasive and in vivo imaging of CGCs. This article presents a comprehensive overview of both the structure and function of CGCs and their alterations in the context of DED, as well as current methods of CGCs imaging assessment. Additionally, potential directions for the visual evaluation of CGCs are discussed.

Deep learning framework for automated goblet cell density analysis in in-vivo rabbit conjunctiva

Goblet cells (GCs) in the conjunctiva are specialized epithelial cells secreting mucins for the mucus layer of protective tear film and playing immune tolerance functions for ocular surface health. Because GC loss is observed in various ocular surface diseases, GC examination is important for precision diagnosis. Moxifloxacin-based fluorescence microscopy (MBFM) was recently developed for non-invasive high-contrast GC visualization. MBFM showed promise for GC examination by high-speed large-area imaging and a robust analysis method is needed to provide GC information. In this study, we developed a deep learning framework for GC image analysis, named dual-channel attention U-Net (DCAU-Net). Dual-channel convolution was used both to extract the overall image texture and to acquire the GC morphological characteristics. A global channel attention module was adopted by combining attention algorithms and channel-wise pooling. DCAU-Net showed 93.1% GC segmentation accuracy and 94.3% GC density estimation accuracy. Further application to both normal and ocular surface damage rabbit models revealed the spatial variations of both GC density and size in normal rabbits and the decreases of both GC density and size in damage rabbit models during recovery after acute damage. The GC analysis results were consistent with histology. Together with the non-invasive high-contrast imaging method, DCAU-Net would provide GC information for the diagnosis of ocular surface diseases.

Noninvasive Imaging of Conjunctival Goblet Cells as a Method for Diagnosing Dry Eye Disease in an Experimental Mouse Model

Purpose

The purpose of this study was to evaluate a noninvasive conjunctival goblet cell (GC) imaging method for assessing dry eye disease (DED) in an experimental mouse model.

Methods

Moxifloxacin-based fluorescence microscopy (MBFM) was used to examine GCs noninvasively in 56 mice. Forty-two (42) DED-induced mice were divided into 2 groups and treated topically for 14 days with cyclosporine (CsA) or normal saline (NS). In vivo MBFM imaging and clinical DED evaluations were performed and goblet cell density (GCD) and goblet cell area (GCA) were obtained and compared with histological GCD using periodic acid-Schiff (PAS) staining. Correlation and receiver operating characteristic (ROC) analyses showed MBFM's high diagnostic value.

Results

The GCD and GCA of the DED mice obtained from in vivo MBFM imaging were highly correlated with clinical DED parameters and GCD obtained from PAS histology. The therapeutic effect of CsA, as observed by in vivo MBFM, was significant with respect to that of NS treatment. The ROC curves derived from in vivo MBFM showed high diagnostic value in assessing DED.

Conclusions

The proposed noninvasive method has high diagnostic value in assessing the severity of DED and the effect of treatment for this disease.

Translational Relevance

A noninvasive imaging method using moxifloxacin-based fluorescence microscopy was evaluated for assessing DED in an experimental mouse model. The method showed high diagnostic value in assessing the severity of DED and the effect of treatment, bridging the gap between basic research and clinical treatment. The study provides a promising tool for diagnosing and monitoring DED.

Moxifloxacin-Based Extended Depth-of-Field Fluorescence Microscopy for Real-Time Conjunctival Goblet Cell Examination

Conjunctival goblet cells (CGCs) are mucin-secreting cells in the eye and play essential roles for ocular surface homeostasis. Since various ocular surface pathologies are related to CGC dysfunction, CGC examination is important for the evaluation of ocular surface conditions. Recently we introduced moxifloxacin-based fluorescence microscopy (MBFM) for non-invasive CGC imaging. However, the imaging speed was up to 1 frame per second (fps) and needed to be improved for clinical applications. In this study, we developed a high-speed moxifloxacin-based, extended depth-of-field (EDOF) microscopy system that operates at a maximum imaging speed of 15 fps. The system used a deformable mirror for the high-speed axial sweeping of focal plane during single-frame acquisitions. The acquired images contained both in-focus and out-of-focus information, and deconvolution was used to filter the in-focus information. The system had a DOF of 800 [Formula: see text], field-of-view of 1.2 mm ×1.2 mm, and resolution of [Formula: see text]. Its performance was demonstrated by real-time, breathing-motion-insensitive CGC imaging of mouse and rabbit models, in vivo. High-speed EDOF microscopy has potentials for non-invasive, real-time CGC examinations of human subjects.