Automated Tortuosity Analysis of Nerve Fibers in Corneal Confocal Microscopy

Cross-modality transfer learning with knowledge infusion for diabetic retinopathy grading

Background

Ultra-wide-field (UWF) fundus photography represents an emerging retinal imaging technique offering a broader field of view, thus enhancing its utility in screening and diagnosing various eye diseases, notably diabetic retinopathy (DR). However, the application of computer-aided diagnosis for DR using UWF images confronts two major challenges. The first challenge arises from the limited availability of labeled UWF data, making it daunting to train diagnostic models due to the high cost associated with manual annotation of medical images. Secondly, existing models' performance requires enhancement due to the absence of prior knowledge to guide the learning process.

Purpose

By leveraging extensively annotated datasets within the field, which encompass large-scale, high-quality color fundus image datasets annotated at either image-level or pixel-level, our objective is to transfer knowledge from these datasets to our target domain through unsupervised domain adaptation.

Methods

Our approach presents a robust model for assessing the severity of diabetic retinopathy (DR) by leveraging unsupervised lesion-aware domain adaptation in ultra-wide-field (UWF) images. Furthermore, to harness the wealth of detailed annotations in publicly available color fundus image datasets, we integrate an adversarial lesion map generator. This generator supplements the grading model by incorporating auxiliary lesion information, drawing inspiration from the clinical methodology of evaluating DR severity by identifying and quantifying associated lesions.

Results

We conducted both quantitative and qualitative evaluations of our proposed method. In particular, among the six representative DR grading methods, our approach achieved an accuracy (ACC) of 68.18% and a precision (pre) of 67.43%. Additionally, we conducted extensive experiments in ablation studies to validate the effectiveness of each component of our proposed method.

Conclusion

In conclusion, our method not only improves the accuracy of DR grading, but also enhances the interpretability of the results, providing clinicians with a reliable DR grading scheme.

Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review

Artificial intelligence (AI) has seen significant progress in medical diagnostics, particularly in image and video analysis. This review focuses on the application of AI in analyzing in vivo confocal microscopy (IVCM) images for corneal diseases. The cornea, as an exposed and delicate part of the body, necessitates the precise diagnoses of various conditions. Convolutional neural networks (CNNs), a key component of deep learning, are a powerful tool for image data analysis. This review highlights AI applications in diagnosing keratitis, dry eye disease, and diabetic corneal neuropathy. It discusses the potential of AI in detecting infectious agents, analyzing corneal nerve morphology, and identifying the subtle changes in nerve fiber characteristics in diabetic corneal neuropathy. However, challenges still remain, including limited datasets, overfitting, low-quality images, and unrepresentative training datasets. This review explores augmentation techniques and the importance of feature engineering to address these challenges. Despite the progress made, challenges are still present, such as the "black-box" nature of AI models and the need for explainable AI (XAI). Expanding datasets, fostering collaborative efforts, and developing user-friendly AI tools are crucial for enhancing the acceptance and integration of AI into clinical practice.

Deep segmentation of OCTA for evaluation and association of changes of retinal microvasculature with Alzheimer's disease and mild cognitive impairment

BACKGROUND

Optical coherence tomography angiography (OCTA) enables fast and non-invasive high-resolution imaging of retinal microvasculature and is suggested as a potential tool in the early detection of retinal microvascular changes in Alzheimer's Disease (AD). We developed a standardised OCTA analysis framework and compared their extracted parameters among controls and AD/mild cognitive impairment (MCI) in a cross-section study.

METHODS

We defined and extracted geometrical parameters of retinal microvasculature at different retinal layers and in the foveal avascular zone (FAZ) from segmented OCTA images obtained using well-validated state-of-the-art deep learning models. We studied these parameters in 158 subjects (62 healthy control, 55 AD and 41 MCI) using logistic regression to determine their potential in predicting the status of our subjects.

RESULTS

In the AD group, there was a significant decrease in vessel area and length densities in the inner vascular complexes (IVC) compared with controls. The number of vascular bifurcations in AD is also significantly lower than that of healthy people. The MCI group demonstrated a decrease in vascular area, length densities, vascular fractal dimension and the number of bifurcations in both the superficial vascular complexes (SVC) and the IVC compared with controls. A larger vascular tortuosity in the IVC, and a larger roundness of FAZ in the SVC, can also be observed in MCI compared with controls.

CONCLUSION

Our study demonstrates the applicability of OCTA for the diagnosis of AD and MCI, and provides a standard tool for future clinical service and research. Biomarkers from retinal OCTA images can provide useful information for clinical decision-making and diagnosis of AD and MCI.

YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation

Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging task due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg. A very essential component of YoloCurvSeg is image synthesis. Specifically, a background generator delivers image backgrounds that closely match the real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.03%, 1.40%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.

A Retinex-based variational model for noise suppression and nonuniform illumination correction in corneal confocal microscopy images

Objective.Corneal confocal microscopy (CCM) image analysis is a non-invasivein vivoclinical technique that can quantify corneal nerve fiber damage. However, the acquired CCM images are often accompanied by speckle noise and nonuniform illumination, which seriously affects the analysis and diagnosis of the diseases.Approach.In this paper, first we propose a variational Retinex model for the inhomogeneity correction and noise removal of CCM images. In this model, the Beppo Levi space is introduced to constrain the smoothness of the illumination layer for the first time, and the fractional order differential is adopted as the regularization term to constrain reflectance layer. Then, a denoising regularization term is also constructed with Block Matching 3D (BM3D) to suppress noise. Finally, by adjusting the uneven illumination layer, we obtain the final results. Second, an image quality evaluation metric is proposed to evaluate the illumination uniformity of images objectively.Main results.To demonstrate the effectiveness of our method, the proposed method is tested on 628 low-quality CCM images from the CORN-2 dataset. Extensive experiments show the proposed method outperforms the other four related methods in terms of noise removal and uneven illumination suppression.SignificanceThis demonstrates that the proposed method may be helpful for the diagnostics and analysis of eye diseases.

Corneal Confocal Microscopy as a Quantitative Imaging Biomarker of Diabetic Peripheral Neuropathy: A Review

Distal symmetric polyneuropathy (DPN), particularly chronic sensorimotor DPN, represents one of the most frequent complications of diabetes, affecting 50% of diabetic patients and causing an enormous financial burden. Whilst diagnostic methods exist to detect and monitor this condition, they have significant limitations, mainly due to their high subjectivity, invasiveness, and non-repeatability. Corneal confocal microscopy (CCM) is an in vivo, non-invasive, and reproducible diagnostic technique for the study of all corneal layers including the sub-basal nerve plexus, which represents part of the peripheral nervous system. We reviewed the current literature on the use of CCM as an instrument in the assessment of diabetic patients, particularly focusing on its role in the study of sub-basal nerve plexus alterations as a marker of DPN. CCM has been demonstrated to be a valid in vivo tool to detect early sub-basal nerve plexus damage in adult and pediatric diabetic patients, correlating with the severity of DPN. Despite its great potential, CCM has still limited application in daily clinical practice, and more efforts still need to be made to allow the dissemination of this technique among doctors taking care of diabetic patients.

Retinal microvasculature and imaging markers of brain frailty in normal aging adults

Background

The retina and brain share a similar embryologic origin, blood barriers, and microvasculature features. Thus, retinal imaging has been of interest in the aging population to help in the early detection of brain disorders. Imaging evaluation of brain frailty, including brain atrophy and markers of cerebral small vessel disease (CSVD), could reflect brain health in normal aging, but is costly and time-consuming. In this study, we aimed to evaluate the retinal microvasculature and its association with radiological indicators of brain frailty in normal aging adults.

Methods

Swept-source optical coherence tomography angiography (SS-OCTA) and 3T-MRI brain scanning were performed on normal aging adults (aged ≥ 50 years). Using a deep learning algorithm, microvascular tortuosity (VT) and fractal dimension parameter (Dbox) were used to evaluate the superficial vascular complex (SVC) and deep vascular complex (DVC) of the retina. MRI markers of brain frailty include brain volumetric measures and CSVD markers that were assessed.

Results

Of the 139 normal aging individuals included, the mean age was 59.43 ± 7.31 years, and 64.0% (n = 89) of the participants were females. After adjustment of age, sex, and vascular risk factors, Dbox in the DVC showed a significant association with the presence of lacunes (β = 0.58, p = 0.007), while VT in the SVC significantly correlated with the score of cerebral deep white matter hyperintensity (β = 0.31, p = 0.027). No correlations were found between brain volumes and retinal microvasculature changes (P > 0.05).

Conclusion

Our report suggests that imaging of the retinal microvasculature may give clues to brain frailty in the aging population.

Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction

Retinal Optical Coherence Tomography Angiography (OCTA) with high-resolution is important for the quantification and analysis of retinal vasculature. However, the resolution of OCTA images is inversely proportional to the field of view at the same sampling frequency, which is not conducive to clinicians for analyzing larger vascular areas. In this paper, we propose a novel Sparse-based domain Adaptation Super-Resolution network (SASR) for the reconstruction of realistic [Formula: see text]/low-resolution (LR) OCTA images to high-resolution (HR) representations. To be more specific, we first perform a simple degradation of the [Formula: see text]/high-resolution (HR) image to obtain the synthetic LR image. An efficient registration method is then employed to register the synthetic LR with its corresponding [Formula: see text] image region within the [Formula: see text] image to obtain the cropped realistic LR image. We then propose a multi-level super-resolution model for the fully-supervised reconstruction of the synthetic data, guiding the reconstruction of the realistic LR images through a generative-adversarial strategy that allows the synthetic and realistic LR images to be unified in the feature domain. Finally, a novel sparse edge-aware loss is designed to dynamically optimize the vessel edge structure. Extensive experiments on two OCTA sets have shown that our method performs better than state-of-the-art super-resolution reconstruction methods. In addition, we have investigated the performance of the reconstruction results on retina structure segmentations, which further validate the effectiveness of our approach.

Centerline extraction by neighborhood-statistics thinning for quantitative analysis of corneal nerve fibers

Objective. Corneal nerve fiber (CNF) has been found to exhibit morphological changes associated with various diseases, which can therefore be utilized to aid in the early diagnosis of those diseases. CNF is usually visualized under corneal confocal microscopy (CCM) in clinic. To obtain the diagnostic biomarkers from CNF image produced from CCM, image processing and quantitative analysis are needed. Usually, CNF is segmented first and then CNF’s centerline is extracted, allowing for measuring geometrical and topological biomarkers of CNF, such as density, tortuosity, and length. Consequently, the accuracy of the segmentation and centerline extraction can make a big impact on the biomarker measurement. Thus, this study is aimed to improve the accuracy and universality of centerline extraction. Approach. We developed a new thinning algorithm based on neighborhood statistics, called neighborhood-statistics thinning (NST), to extract the centerline of CNF. Compared with traditional thinning and skeletonization techniques, NST exhibits a better capability to preserve the fine structure of CNF which can effectively benefit the biomarkers measurement above. Moreover, NST incorporates a fitting process, which can make centerline extraction be less influenced by image segmentation. Main results. This new method is evaluated on three datasets which are segmented with five different deep learning networks. The results show that NST is superior to thinning and skeletonization on all the CNF-segmented datasets with a precision rate above 0.82. Last, NST is attempted to be applied for the diagnosis of keratitis with the quantitative biomarkers measured from the extracted centerlines. Longer length and higher density but lower tortuosity were found on the CNF of keratitis patients as compared to healthy patients. Significance. This demonstrates that NST has a good potential to aid in the diagnostics of eye diseases in clinic.

Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes

AIMS/HYPOTHESIS

We aimed to develop an artificial intelligence (AI)-based deep learning algorithm (DLA) applying attribution methods without image segmentation to corneal confocal microscopy images and to accurately classify peripheral neuropathy (or lack of).

METHODS

The AI-based DLA utilised convolutional neural networks with data augmentation to increase the algorithm's generalisability. The algorithm was trained using a high-end graphics processor for 300 epochs on 329 corneal nerve images and tested on 40 images (1 image/participant). Participants consisted of healthy volunteer (HV) participants (n = 90) and participants with type 1 diabetes (n = 88), type 2 diabetes (n = 141) and prediabetes (n = 50) (defined as impaired fasting glucose, impaired glucose tolerance or a combination of both), and were classified into HV, those without neuropathy (PN-) (n = 149) and those with neuropathy (PN+) (n = 130). For the AI-based DLA, a modified residual neural network called ResNet-50 was developed and used to extract features from images and perform classification. The algorithm was tested on 40 participants (15 HV, 13 PN-, 12 PN+). Attribution methods gradient-weighted class activation mapping (Grad-CAM), Guided Grad-CAM and occlusion sensitivity displayed the areas within the image that had the greatest impact on the decision of the algorithm.

RESULTS

The results were as follows: HV: recall of 1.0 (95% CI 1.0, 1.0), precision of 0.83 (95% CI 0.65, 1.0), F₁-score of 0.91 (95% CI 0.79, 1.0); PN-: recall of 0.85 (95% CI 0.62, 1.0), precision of 0.92 (95% CI 0.73, 1.0), F₁-score of 0.88 (95% CI 0.71, 1.0); PN+: recall of 0.83 (95% CI 0.58, 1.0), precision of 1.0 (95% CI 1.0, 1.0), F₁-score of 0.91 (95% CI 0.74, 1.0). The features displayed by the attribution methods demonstrated more corneal nerves in HV, a reduction in corneal nerves for PN- and an absence of corneal nerves for PN+ images.

CONCLUSIONS/INTERPRETATION

We demonstrate promising results in the rapid classification of peripheral neuropathy using a single corneal image. A large-scale multicentre validation study is required to assess the utility of AI-based DLA in screening and diagnostic programmes for diabetic neuropathy.

Hybrid Variation-Aware Network for Angle-Closure Assessment in AS-OCT

Automatic angle-closure assessment in Anterior Segment OCT (AS-OCT) images is an important task for the screening and diagnosis of glaucoma, and the most recent computer-aided models focus on a binary classification of anterior chamber angles (ACA) in AS-OCT, i.e., open-angle and angle-closure. In order to assist clinicians who seek better to understand the development of the spectrum of glaucoma types, a more discriminating three-class classification scheme was suggested, i.e., the classification of ACA was expended to include open-, appositional- and synechial angles. However, appositional and synechial angles display similar appearances in an AS-OCT image, which makes classification models struggle to differentiate angle-closure subtypes based on static AS-OCT images. In order to tackle this issue, we propose a 2D-3D Hybrid Variation-aware Network (HV-Net) for open-appositional-synechial ACA classification from AS-OCT imagery. Specifically, taking into account clinical priors, we first reconstruct the 3D iris surface from an AS-OCT sequence, and obtain the geometrical characteristics necessary to provide global shape information. 2D AS-OCT slices and 3D iris representations are then fed into our HV-Net to extract cross-sectional appearance features and iris morphological features, respectively. To achieve similar results to those of dynamic gonioscopy examination, which is the current gold standard for diagnostic angle assessment, the paired AS-OCT images acquired in dark and light illumination conditions are used to obtain an accurate characterization of configurational changes in ACAs and iris shapes, using a Variation-aware Block. In addition, an annealing loss function was introduced to optimize our model, so as to encourage the sub-networks to map the inputs into the more conducive spaces to extract dark-to-light variation representations, while retaining the discriminative power of the learned features. The proposed model is evaluated across 1584 paired AS-OCT samples, and it has demonstrated its superiority in classifying open-, appositional- and synechial angles.

Structure and Illumination Constrained GAN for Medical Image Enhancement

The development of medical imaging techniques has greatly supported clinical decision making. However, poor imaging quality, such as non-uniform illumination or imbalanced intensity, brings challenges for automated screening, analysis and diagnosis of diseases. Previously, bi-directional GANs (e.g., CycleGAN), have been proposed to improve the quality of input images without the requirement of paired images. However, these methods focus on global appearance, without imposing constraints on structure or illumination, which are essential features for medical image interpretation. In this paper, we propose a novel and versatile bi-directional GAN, named Structure and illumination constrained GAN (StillGAN), for medical image quality enhancement. Our StillGAN treats low- and high-quality images as two distinct domains, and introduces local structure and illumination constraints for learning both overall characteristics and local details. Extensive experiments on three medical image datasets (e.g., corneal confocal microscopy, retinal color fundus and endoscopy images) demonstrate that our method performs better than both conventional methods and other deep learning-based methods. In addition, we have investigated the impact of the proposed method on different medical image analysis and clinical tasks such as nerve segmentation, tortuosity grading, fovea localization and disease classification.

Multi-discriminator adversarial convolutional network for nerve fiber segmentation in confocal corneal microscopy images

Quantitative measurements of corneal sub-basal nerves are biomarkers for many ocular surface disorders, and are also important for early diagnosis and assessment of progression of neurodegenerative diseases. This paper aims to develop an automatic method for nerve fiber segmentation from in vivo corneal confocal microscopy (CCM) images, which is fundamental for nerve morphology quantification. A novel multi-discriminator adversarial convolutional network (MDACN) is proposed, where both the generator and the two discriminators emphasize multi-scale feature representations. The generator is a U-shaped fully convolutional network with multi-scale split and concatenate blocks, and the two discriminators have different effective receptive fields, sensitive to features of different scales. A novel loss function is also proposed which enables the network to pay more attention to thin fibers. The MDACN framework was evaluated on four datasets. Experiment results show that our method has excellent segmentation performance for corneal nerve fibers and outperforms some state-of-the-art methods.

Corneal nerve changes following treatment with neurotoxic anticancer drugs

Survival rates of cancer has improved with the development of anticancer drugs including systemic chemotherapeutic agents. However, long-lasting side effects could impact treated patients. Neurotoxic anticancer drugs are specific agents which cause chemotherapy-induced peripheral neuropathy (CIPN), a debilitating condition that severely deteriorates quality of life of cancer patients and survivors. The ocular surface is also prone to neurotoxicity but investigation into the effects of neurotoxic chemotherapy on the ocular surface has been more limited compared to other systemic etiologies such as diabetes. There is also no standardized protocol for CIPN diagnosis with an absence of a reliable, objective method of observing nerve damage structurally. As the cornea is the most densely innervated region of the body, researchers have started to focus on corneal neuropathic changes that are associated with neurotoxic chemotherapy treatment. In-vivo corneal confocal microscopy enables rapid and objective structural imaging of ocular surface microscopic structures such as corneal nerves, while esthesiometers provide means of functional assessment by examining corneal sensitivity. The current article explores the current guidelines and gaps in our knowledge of CIPN diagnosis and the potential role of in-vivo corneal confocal microscopy as a diagnostic or prognostic tool. Corneal neuropathic changes with neurotoxic anticancer drugs from animal research progressing through to human clinical studies are also discussed, with a focus on how these data inform our understanding of CIPN.