Development of a Reliable Automated Algorithm for the Morphometric Analysis of Human Corneal Endothelium

Applications of Imaging Technologies in Fuchs Endothelial Corneal Dystrophy: A Narrative Literature Review

Fuchs endothelial corneal dystrophy (FECD) is a complex genetic disorder characterized by the slow and progressive degeneration of corneal endothelial cells. Thus, it may result in corneal endothelial decompensation and irreversible corneal edema. Moreover, FECD is associated with alterations in all corneal layers, such as thickening of the Descemet membrane, stromal scarring, subepithelial fibrosis, and the formation of epithelial bullae. Hence, anterior segment imaging devices that enable precise measurement of functional and anatomical changes in the cornea are essential for the management of FECD. In this review, the authors will introduce studies on the application of various imaging modalities, such as anterior segment optical coherence tomography, Scheimpflug corneal tomography, specular microscopy, in vitro confocal microscopy, and retroillumination photography, in the diagnosis and monitoring of FECD and discuss the results of these studies. The application of novel technologies, including image processing technology and artificial intelligence, that are expected to further enhance the accuracy, precision, and speed of the imaging technologies will also be discussed.

Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps

Specular microscopy assessment of the human corneal endothelium (CE) in Fuchs' dystrophy is challenging due to the presence of dark image regions called guttae. This paper proposes a UNet-based segmentation approach that requires minimal post-processing and achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs' dystrophy. We cast the segmentation problem as a regression task of the cell and gutta signed distance maps instead of a pixel-level classification task as typically done with UNets. Compared to the conventional UNet classification approach, the distance-map regression approach converges faster in clinically relevant parameters. It also produces morphometric parameters that agree with the manually-segmented ground-truth data, namely the average cell density difference of -41.9 cells/mm2 (95% confidence interval (CI) [-306.2, 222.5]) and the average difference of mean cell area of 14.8 µm 2 (95% CI [-41.9, 71.5]). These results suggest a promising alternative for CE assessment.

DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae

Corneal guttae, which are the abnormal growth of extracellular matrix in the corneal endothelium, are observed in specular images as black droplets that occlude the endothelial cells. To estimate the corneal parameters (endothelial cell density [ECD], coefficient of variation [CV], and hexagonality [HEX]), we propose a new deep learning method that includes a novel attention mechanism (named fNLA), which helps to infer the cell edges in the occluded areas. The approach first derives the cell edges, then infers the well-detected cells, and finally employs a postprocessing method to fix mistakes. This results in a binary segmentation from which the corneal parameters are estimated. We analyzed 1203 images (500 contained guttae) obtained with a Topcon SP-1P microscope. To generate the ground truth, we performed manual segmentation in all images. Several networks were evaluated (UNet, ResUNeXt, DenseUNets, UNet++, etc.) and we found that DenseUNets with fNLA provided the lowest error: a mean absolute error of 23.16 [cells/mm[Formula: see text]] in ECD, 1.28 [%] in CV, and 3.13 [%] in HEX. Compared with Topcon's built-in software, our error was 3-6 times smaller. Overall, our approach handled notably well the cells affected by guttae, detecting cell edges partially occluded by small guttae and discarding large areas covered by extensive guttae.

U-Net Convolutional Neural Network for Segmenting the Corneal Endothelium in a Mouse Model of Fuchs Endothelial Corneal Dystrophy

PURPOSE

The purpose of this study was to assess the U-Net-based convolutional neural network performance for segmenting corneal endothelium and guttae of Fuchs endothelial corneal dystrophy.

METHODS

Twenty-eight images of corneal endothelial cells and guttae of Col8a2L450W/L450W knock-in mice were obtained by specular microscopy. We used 20 images as training data to develop the U-Net for analyzing guttae and cell borders. The proposed network was validated using independent test data of 8 images. Cell density, hexagonality, and coefficient of variation were calculated from the predicted cell borders and compared with ground truth.

RESULTS

U-Net allowed the prediction of cell borders and guttae, and overlays of those segmentations on specular microscopy images highly corresponded to ground truth. The average number of guttae per field was 6.25 ± 8.07 for ground truth and 6.25 ± 7.87 when predicted by the network (Pearson correlation coefficient 0.989, P = 3.25 × 10 -6 ). The guttae areas were 1.60% ± 1.79% by manual determination and 1.90% ± 2.02% determined by the network (Pearson correlation coefficient 0.970, P = 6.72 × 10 -5 ). Cell density, hexagonality, and coefficient of variation analyzed by the proposed network for cell borders showed very strong correlations with ground truth (Pearson correlation coefficient 0.989, P = 3.23 × 10 -6 , Pearson correlation coefficient 0.978, P = 2.66 × 10 -5 , and Pearson correlation coefficient 0.936, P = 6.20 × 10 -4 , respectively).

CONCLUSIONS

We demonstrated proof of concept for application of U-Net for objective analysis of corneal endothelial cells and guttae in Fuchs endothelial corneal dystrophy, based on limited ground truth data.

A Fully Automated Segmentation and Morphometric Parameter Estimation System for Assessing Corneal Endothelial Cell Images

PURPOSE

To develop a fully automated segmentation and morphometric parameter estimation system for assessing corneal endothelial cells from in vivo confocal microscopy images.

DESIGN

Artificial intelligence (neural network) study.

METHODS

First, a fully automated deep learning system for assessing corneal endothelial cells was developed using the development set (from 99 subjects). Second, 184 images (from 97 subjects) were used to construct the testing set to evaluate the clinical validity and usefulness of the automated segmentation and morphometric system. Third, the automatically calculated endothelial cell density (ECD) values, Topcon's cell density, and manually calculated ECD were compared.

RESULTS

After slit lamp examination, 88 healthy subjects, 2 Fuchs endothelial dystrophy patients, and 7 corneal endotheliitis patients were identified among the 97 subjects in the testing set. The automatedly estimated morphometric parameters for the testing set were an average number of 234 cells, an ECD of 2592 cells/mm², a coefficient of variation in the cell area of 32.14%, and a percentage of hexagonal cells of 54.16%. Pearson's correlation coefficient between the automated ECD and Topcon's cell density and between the manually calculated ECD and Topcon's cell density was 0.932 (P < .01) and 0.818 (P < .01), respectively. The Bland-Altman plot of Topcon's cell density and the automated ECD yielded 95% limits of agreement between 271.94 and -572.46 (concordance correlation coefficient = 0.9).

CONCLUSIONS

A fully automated method for segmenting corneal endothelial cells and estimating morphometric parameters using in vivo confocal microscopy images is more efficient and accurate for assessing the normal corneal endothelium.

Trainable watershed-based model for cornea endothelial cell segmentation

Segmentation of the medical image plays a significant role when it comes to diagnosis using computer aided system. This article focuses on the human corneal endothelium’s health, which is one of the filed research interests, especially in the human cornea. Various pathological environments fasten the extermination of the endothelial cells, which in turn decreases the cell density in an abnormal manner. Dead cells worsen the hexagonal design. The mutilated endothelial cells can no longer revive back and that gives room for neighbouring cells to migrate and expand so that they can fill in the space. The latter results in cell elongation that is unpredictable as well as increase in size and thinning. Cell density and shape are therefore considered major parameters when it comes to explaining the health condition attributed to corneal endothelium. In this study, medical feature extraction was obtained depending on the segmentation of the endothelial cell boundary, and the task of segmentation of such objects especially the thin, transparent, and unclear cell boundary is considered challenging due to the nature of the image capture during endothelium layer examination by ophthalmologists using confocal or specular microscopy. The resulting image suffers from various issues that affect the quality of the image. Low quality is due to non-uniformity of illumination and the presence of a lot of noise and artefacts resulting from high amounts of distortion, and most of these limitations are present because of the nature of the imaging modality. Usually, images contain certain kind of noise and also continuous shadow. Furthermore, the cells are separated by poor border, thereby leading to great difficulty in the segmentation of the images. The irregular shape of cell and also the contrast of such images seem to be low as they possess blurry boundaries with diverse objects existing in addition to the lack of homogeneity. The main aim of the study is to propose and develop a totally automatic, robust, and real-time model for the segmentation of endothelial cells of the human cornea obtained by in vivo microscopy and computation of different clinical features of endothelial cells. To achieve the aim of this study a new scheme of image enhancement was proposed such as the Contrast-Limited Adaptive Histogram Equalisation (CLAHE) technique to enhance contrast. After that, a new image denoising technique called Wavelet Transform Filter and Butterworth Bandpass for Segmentation is used. Subsequently, brightness level correction is applied by using the moving average filter and the CLAHE to reduce the effects of the non-uniform image lighting produced as a result of the previous step. The main aim of this article is the segmentation of endothelial cells, which involves precise detection of the endothelial contours. So a new segmentation model was proposed such that the shape of the cells will be extracted, and the contours were highlighted. This stage is followed by clinical feature extraction and uses the features for diagnosis. In this stage, several relevant clinical features such as pleomorphism mean cell perimeter, mean cell density, mean cell area, and polymegathism are extracted. The role of these clinical features is crucial for the early detection of corneal pathologies as well as the evaluation of the health of the corneal endothelium layer. The findings of this study were promising.

A Review On digital image processing techniques for in-Vivo confocal images of the cornea

This work reviews the scientific literature regarding digital image processing for in vivo confocal microscopy images of the cornea. We present and discuss a selection of prominent techniques designed for semi- and automatic analysis of four areas of the cornea (epithelium, sub-basal nerve plexus, stroma and endothelium). The main context is image enhancement, detection of structures of interest, and quantification of clinical information. We have found that the preprocessing stage lacks of quantitative studies regarding the quality of the enhanced image, or its effects in subsequent steps of the image processing. Threshold values are widely used in the reviewed methods, although generally, they are selected empirically and manually. The image processing results are evaluated in many cases through comparison with gold standards not widely accepted. It is necessary to standardize values to be quantified in terms of sensitivity and specificity of methods. Most of the reviewed studies do not show an estimation of the computational cost of the image processing. We conclude that reliable, automatic, computer-assisted image analysis of the cornea is still an open issue, constituting an interesting and worthwhile area of research.

Artificial Intelligence in Cornea, Refractive Surgery, and Cataract: Basic Principles, Clinical Applications, and Future Directions

ABSTRACT

Corneal diseases, uncorrected refractive errors, and cataract represent the major causes of blindness globally. The number of refractive surgeries, either cornea- or lens-based, is also on the rise as the demand for perfect vision continues to increase. With the recent advancement and potential promises of artificial intelligence (AI) technologies demonstrated in the realm of ophthalmology, particularly retinal diseases and glaucoma, AI researchers and clinicians are now channeling their focus toward the less explored ophthalmic areas related to the anterior segment of the eye. Conditions that rely on anterior segment imaging modalities, including slit-lamp photography, anterior segment optical coherence tomography, corneal tomography, in vivo confocal microscopy and/or optical biometers, are the most commonly explored areas. These include infectious keratitis, keratoconus, corneal grafts, ocular surface pathologies, preoperative screening before refractive surgery, intraocular lens calculation, and automated refraction, among others. In this review, we aimed to provide a comprehensive update on the utilization of AI in anterior segment diseases, with particular emphasis on the recent advancement in the past few years. In addition, we demystify some of the basic principles and terminologies related to AI, particularly machine learning and deep learning, to help improve the understanding, research and clinical implementation of these AI technologies among the ophthalmologists and vision scientists. As we march toward the era of digital health, guidelines such as CONSORT-AI, SPIRIT-AI, and STARD-AI will play crucial roles in guiding and standardizing the conduct and reporting of AI-related trials, ultimately promoting their potential for clinical translation.

Unbiased corneal tissue analysis using Gabor-domain optical coherence microscopy and machine learning for automatic segmentation of corneal endothelial cells

SIGNIFICANCE

An accurate, automated, and unbiased cell counting procedure is needed for tissue selection for corneal transplantation.

AIM

To improve accuracy and reduce bias in endothelial cell density (ECD) quantification by combining Gabor-domain optical coherence microscopy (GDOCM) for three-dimensional, wide field-of-view (1  mm2) corneal imaging and machine learning for automatic delineation of endothelial cell boundaries.

APPROACH

Human corneas stored in viewing chambers were imaged over a wide field-of-view with GDOCM without contacting the specimens. Numerical methods were applied to compensate for the natural curvature of the cornea and produce an image of the flattened endothelium. A convolutional neural network (CNN) was trained to automatically delineate the cell boundaries using 180 manually annotated images from six corneas. Ten additional corneas were imaged with GDOCM and compared with specular microscopy (SM) to determine performance of the combined GDOCM and CNN to achieve automated endothelial counts relative to current procedural standards.

RESULTS

Cells could be imaged over a larger area with GDOCM than SM, and more cells could be delineated via automatic cell segmentation than via manual methods. ECD obtained from automatic cell segmentation of GDOCM images yielded a correlation of 0.94 (p  <  0.001) with the manual segmentation on the same images, and correlation of 0.91 (p  <  0.001) with the corresponding manually counted SM results.

CONCLUSIONS

Automated endothelial cell counting on GDOCM images with large field of view eliminates selection bias and reduces sampling error, which both affect the gold standard of manual counting on SM images.

Deep Learning for Assessing the Corneal Endothelium from Specular Microscopy Images up to 1 Year after Ultrathin-DSAEK Surgery

Purpose

To present a fully automatic method to estimate the corneal endothelium parameters from specular microscopy images and to use it to study a one-year follow-up after ultrathin Descemet stripping automated endothelial keratoplasty.

Methods

We analyzed 383 post ultrathin Descemet stripping automated endothelial keratoplasty images from 41 eyes acquired with a Topcon SP-1P specular microscope at 1, 3, 6, and 12 months after surgery. The estimated parameters were endothelial cell density (ECD), coefficient of variation (CV), and hexagonality (HEX). Manual segmentation was performed in all images.

Results

Our method provided an estimate for ECD, CV, and HEX in 98.4% of the images, whereas Topcon's software had a success rate of 71.5% for ECD/CV and 30.5% for HEX. For the images with estimates, the percentage error in our method was 2.5% for ECD, 5.7% for CV, and 5.7% for HEX, whereas Topcon's software provided an error of 7.5% for ECD, 17.5% for CV, and 18.3% for HEX. Our method was significantly better than Topcon's (P < 0.0001) and was not statistically significantly different from the manual assessments (P > 0.05). At month 12, the subjects presented an average ECD = 1377 ± 483 [cells/mm2], CV = 26.1 ± 5.7 [%], and HEX = 58.1 ± 7.1 [%].

Conclusions

The proposed method obtains reliable and accurate estimations even in challenging specular images of pathologic corneas.

Translational Relevance

CV and HEX, not currently used in the clinic owing to a lack of reliability in automatic methods, are useful biomarkers to analyze the postoperative healing process. Our accurate estimations allow now for their clinical use.

Convolutional neural network-based regression for biomarker estimation in corneal endothelium microscopy images

The morphometric parameters of the corneal endothelium - cell density (ECD), cell size variation (CV), and hexagonality (HEX) - provide clinically relevant information about the cornea. To estimate these parameters, the endothelium is commonly imaged with a non-contact specular microscope and cell segmentation is performed to these images. In previous work, we have developed several methods that, combined, can perform an automated estimation of the parameters: the inference of the cell edges, the detection of the region of interest (ROI), a post-processing method that combines both images (edges and ROI), and a refinement method that removes false edges. In this work, we first explore the possibility of using a CNN-based regressor to directly infer the parameters from the edge images, simplifying the framework. We use a dataset of 738 images coming from a study related to the implantation of a Baerveldt glaucoma device and a standard clinical care regarding DSAEK corneal transplantation, both from the Rotterdam Eye Hospital and both containing images of unhealthy endotheliums. This large dataset allows us to build a large training set that makes this approach feasible. We achieved a mean absolute percentage error (MAPE) of 4.32% for ECD, 7.07% for CV, and 11.74% for HEX. These results, while promising, do not outperform our previous work. In a second experiment, we explore the use of the CNN-based regressor to improve the post-processing method of our previous approach in order to adapt it to the specifics of each image. Our results showed no clear benefit and proved that our previous post-processing is already highly reliable and robust.

Automated segmentation of the corneal endothelium in a large set of 'real-world' specular microscopy images using the U-Net architecture

Monitoring the density of corneal endothelial cells (CEC) is essential in the management of corneal diseases. Its manual calculation is time consuming and prone to errors. U-Net, a neural network for biomedical image segmentation, has shown promising results in the automated segmentation of images of healthy corneas and good quality. The purpose of this study was to assess its performance in “real-world” CEC images (variable quality, different ophthalmologic diseases). The outcome measures were: precision and recall of the extraction of CEC, correctness of CEC density estimation, detection of ungradable images. A classical approach based on grayscale morphology and water shedding was pursued for comparison. There was good agreement between the automated image analysis and the manual annotation from the U-Net. R-square from Pearson’s correlation was 0.96. Recall of CEC averaged 0.34 and precision 0.84. The U-Net correctly predicted the CEC density in a large set of images of healthy and diseased corneas, including images of poor quality. It robustly ignored image regions with poor visibility of CEC. The classical approach, however, did not provide acceptable results. R-square from Pearson’s correlation with the ground truth was as low as 0.35.

Fully convolutional architecture vs sliding-window CNN for corneal endothelium cell segmentation

Background

Corneal endothelium (CE) images provide valuable clinical information regarding the health state of the cornea. Computation of the clinical morphometric parameters requires the segmentation of endothelial cell images. Current techniques to image the endothelium in vivo deliver low quality images, which makes automatic segmentation a complicated task. Here, we present two convolutional neural networks (CNN) to segment CE images: a global fully convolutional approach based on U-net, and a local sliding-window network (SW-net). We propose to use probabilistic labels instead of binary, we evaluate a preprocessing method to enhance the contrast of images, and we introduce a postprocessing method based on Fourier analysis and watershed to convert the CNN output images into the final cell segmentation. Both methods are applied to 50 images acquired with an SP-1P Topcon specular microscope. Estimates are compared against a manual delineation made by a trained observer.

Results

U-net (AUC=0.9938) yields slightly sharper, clearer images than SW-net (AUC=0.9921). After postprocessing, U-net obtains a DICE=0.981 and a MHD=0.22 (modified Hausdorff distance), whereas SW-net yields a DICE=0.978 and a MHD=0.30. U-net generates a wrong cell segmentation in only 0.48% of the cells, versus 0.92% for the SW-net. U-net achieves statistically significant better precision and accuracy than both, Topcon and SW-net, for the estimates of three clinical parameters: cell density (ECD), polymegethism (CV), and pleomorphism (HEX). The mean relative error in U-net for the parameters is 0.4% in ECD, 2.8% in CV, and 1.3% in HEX. The computation time to segment an image and estimate the parameters is barely a few seconds.

Conclusions

Both methods presented here provide a statistically significant improvement over the state of the art. U-net has reached the smallest error rate. We suggest a segmentation refinement based on our previous work to further improve the performance.

Corneal Endothelial Cell Segmentation by Classifier-Driven Merging of Oversegmented Images

Corneal endothelium images obtained by in vivo specular microscopy provide important information to assess the health status of the cornea. Estimation of clinical parameters, such as cell density, polymegethism, and pleomorphism, requires accurate cell segmentation. State-of-the-art techniques to automatically segment the endothelium are error-prone when applied to images with low contrast and/or large variation in cell size. Here, we propose an automatic method to segment the endothelium. Starting with an oversegmented image comprised of superpixels obtained from a stochastic watershed segmentation, the proposed method uses intensity and shape information of the superpixels to identify and merge those that constitute a cell, using support vector machines. We evaluated the automatic segmentation on a data set of in vivo specular microscopy images (Topcon SP-1P), obtaining 95.8% correctly merged cells and 2.0% undersegmented cells. We also evaluated the parameter estimation against the results of the vendor's built-in software, obtaining a statistically significant better precision in all parameters and a similar or better accuracy. The parameter estimation was also evaluated on three other data sets from different imaging modalities (confocal microscopy, phase-contrast microscopy, and fluorescence confocal microscopy) and tissue types (ex vivo corneal endothelium and retinal pigment epithelium). In comparison with the estimates of the data sets' authors, we achieved statistically significant better accuracy and precision in all parameters except pleomorphism, where a similar accuracy and precision were obtained.