A fully automated cell segmentation and morphometric parameter system for quantifying corneal endothelial cell morphology

YesBackground and Objective Corneal endothelial cell abnormalities may be associated with a number of corneal and systemic diseases. Damage to the endothelial cells can significantly affect corneal transparency by altering hydration of the corneal stroma, which can lead to irreversible endothelial cell pathology requiring corneal transplantation. To date, quantitative analysis of endothelial cell abnormalities has been manually performed by ophthalmologists using time consuming and highly subjective semi-automatic tools, which require an operator interaction. We developed and applied a fully-automated and real-time system, termed the Corneal Endothelium Analysis System (CEAS) for the segmentation and computation of endothelial cells in images of the human cornea obtained by in vivo corneal confocal microscopy. Methods First, a Fast Fourier Transform (FFT) Band-pass filter is applied to reduce noise and enhance the image quality to make the cells more visible. Secondly, endothelial cell boundaries are detected using watershed transformations and Voronoi tessellations to accurately quantify the morphological parameters of the human corneal endothelial cells. The performance of the automated segmentation system was tested against manually traced ground-truth images based on a database consisting of 40 corneal confocal endothelial cell images in terms of segmentation accuracy and obtained clinical features. In addition, the robustness and efficiency of the proposed CEAS system were compared with manually obtained cell densities using a separate database of 40 images from controls (n = 11), obese subjects (n = 16) and patients with diabetes (n = 13). Results The Pearson correlation coefficient between automated and manual endothelial cell densities is 0.9 (p < 0.0001) and a Bland–Altman plot shows that 95% of the data are between the 2SD agreement lines. Conclusions We demonstrate the effectiveness and robustness of the CEAS system, and the possibility of utilizing it in a real world clinical setting to enable rapid diagnosis and for patient follow-up, with an execution time of only 6 seconds per image

Novel medical imaging technologies for processing epithelium and endothelium layers in corneal confocal images. Developing automated segmentation and quantification algorithms for processing sub-basal epithelium nerves and endothelial cells for early diagnosis of diabetic neuropathy in corneal confocal microscope images

Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the corneal epithelium nerve structures and the corneal endothelial cell can assist early diagnosis of this disease and other corneal diseases, which can lead to visual impairment and then to blindness. In this thesis, fully-automated segmentation and quantification algorithms for processing and analysing sub-basal epithelium nerves and endothelial cells are proposed for early diagnosis of diabetic neuropathy in Corneal Confocal Microscopy (CCM) images. Firstly, a fully automatic nerve segmentation system for corneal confocal microscope images is proposed. The performance of the proposed system is evaluated against manually traced images with an execution time of the prototype is 13 seconds. Secondly, an automatic corneal nerve registration system is proposed. The main aim of this system is to produce a new informative corneal image that contains structural and functional information. Thirdly, an automated real-time system, termed the Corneal Endothelium Analysis System (CEAS) is developed and applied for the segmentation of endothelial cells in images of human cornea obtained by In Vivo CCM. The performance of the proposed CEAS system was tested against manually traced images with an execution time of only 6 seconds per image. Finally, the results obtained from all the proposed approaches have been evaluated and validated by an expert advisory board from two institutes, they are the Division of Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar and the Manchester Royal Eye Hospital, Centre for Endocrinology and Diabetes, UK

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.This work has been partly funded by Ministerio de Ciencia, Tecnología e Innovación, Colombia, Project 124489786239 (Contract 763-2021), Universidad Tecnológica de Bolívar (UTB) Project CI2021P02, and Agencia Estatal de Investigación del Gobierno de España (PID2020-114582RB-I00/ AEI / 10.13039/501100011033). J. Sierra thanks UTB for a post-graduate scholarship.Peer ReviewedPostprint (published version

Development of Novel Diagnostic Tools for Dry Eye Disease using Infrared Meibography and In Vivo Confocal Microscopy

Dry eye disease (DED) is a multifactorial disease of the ocular surface where tear film instability, hyperosmolarity, neurosensory abnormalities, meibomian gland dysfunction, ocular surface inflammation and damage play a dedicated etiological role. Estimated 5 to 50% of the world population in different demographic locations, age and gender are currently affected by DED. The risk and occurrence of DED increases at a significant rate with age, which makes dry eye a major growing public health issue. DED not only impacts the patient’s quality of vision and life, but also creates a socio-economic burden of millions of euros per year. DED diagnosis and monitoring can be a challenging task in clinical practice due to the multifactorial nature and the poor correlation between signs and symptoms. Key clinical diagnostic tests and techniques for DED diagnosis include tearfilm break up time, tear secretion – Schirmer’s test, ocular surface staining, measurement of osmolarity, conjunctival impression cytology. However, these clinical diagnostic techniques are subjective, selective, require contact, and are unpleasant for the patient’s eye. Currently, new advances in different state-of-the-art imaging modalities provide non-invasive, non- or semi-contact, and objective parameters that enable objective evaluation of DED diagnosis. Among the different and constantly evolving imaging modalities, some techniques are developed to assess morphology and function of meibomian glands, and microanatomy and alteration of the different ocular surface tissues such as corneal nerves, immune cells, microneuromas, and conjunctival blood vessels. These clinical parameters cannot be measured by conventional clinical assessment alone. The combination of these imaging modalities with clinical feedback provides unparalleled quantification information of the dynamic properties and functional parameters of different ocular surface tissues. Moreover, image-based biomarkers provide objective, specific, and non / marginal contact diagnosis, which is faster and less unpleasant to the patient’s eye than the clinical assessment techniques. The aim of this PhD thesis was to introduced deep learning-based novel computational methods to segment and quantify meibomian glands (both upper and lower eyelids), corneal nerves, and dendritic cells. The developed methods used raw images, directly export from the clinical devices without any image pre-processing to generate segmentation masks. Afterward, it provides fully automatic morphometric quantification parameters for more reliable disease diagnosis. Noteworthily, the developed methods provide complete segmentation and quantification information for faster disease characterization. Thus, the developed methods are the first methods (especially for meibomian gland and dendritic cells) to provide complete morphometric analysis. Taken together, we have developed deep learning based automatic system to segment and quantify different ocular surface tissues related to DED namely, meibomian gland, corneal nerves, and dendritic cells to provide reliable and faster disease characterization. The developed system overcomes the current limitations of subjective image analysis and enables precise, accurate, reliable, and reproducible ocular surface tissue analysis. These systems have the potential to make an impact clinically and in the research environment by specifying faster disease diagnosis, facilitating new drug development, and standardizing clinical trials. Moreover, it will allow both researcher and clinicians to analyze meibomian glands, corneal nerves, and dendritic cells more reliably while reducing the time needed to analyze patient images significantly. Finally, the methods developed in this research significantly increase the efficiency of evaluating clinical images, thereby supporting and potentially improving diagnosis and treatment of ocular surface disease

Corneal confocal microscopy detects a reduction in corneal endothelial cells and nerve fibres in patients with acute ischemic stroke

YesEndothelial dysfunction and damage underlie cerebrovascular disease and ischemic stroke. We undertook corneal confocal microscopy (CCM) to quantify corneal endothelial cell and nerve morphology in 146 patients with an acute ischemic stroke and 18 age-matched healthy control participants. Corneal endothelial cell density was lower (P<0.001) and endothelial cell area (P<0.001) and perimeter (P<0.001) were higher, whilst corneal nerve fbre density (P<0.001), corneal nerve branch density (P<0.001) and corneal nerve fbre length (P=0.001) were lower in patients with acute ischemic stroke compared to controls. Corneal endothelial cell density, cell area and cell perimeter correlated with corneal nerve fber density (P=0.033, P=0.014, P=0.011) and length (P=0.017, P=0.013, P=0.008), respectively. Multiple linear regression analysis showed a signifcant independent association between corneal endothelial cell density, area and perimeter with acute ischemic stroke and triglycerides. CCM is a rapid non-invasive ophthalmic imaging technique, which could be used to identify patients at risk of acute ischemic stroke.Qatar National Research Fund Grant BMRP2003865

Combining Smart Material Platforms and New Computational Tools to Investigate Cell Motility Behavior and Control

Cell-extracellular matrix (ECM) interactions play a critical role in regulating important biological phenomena, including morphogenesis, tissue repair, and disease states. In vivo, cells are subjected to various mechanical, chemical, and electrical cues to collectively guide their functionality within a specific microenvironment. To better understand the mechanisms regulating cell adhesive, differentiation, and motility dynamics, researchers have developed in vitro platforms to synthetically mimic native tissue responses. While important information about cell-ECM interactions have been revealed using these systems, a knowledge gap currently exists regarding how cell responses in static environments relate to the dynamic cell-ECM interaction behaviors observed in vivo. Advances at the intersection of materials science, biophysics, and cell biology have recently enabled the production of dynamic ECM mimics where cells can be exposed to controlled mechanical, electrical or chemical cues to directly decouple cell-ECM related behaviors from cell-cell or cell-environmental factors. Utilization of these dynamic synthetic biomaterials will enable discovery of novel mechanisms fundamental in tissue development, homeostasis, repair, and disease. In this dissertation, the primary goal was to evaluate how mechanical changes in the ECM regulate cell motility and polarization responses. This was accomplished through two major aims: 1) by developing a modular image processing tool that could be applied in complex synthetic in vitro microenvironments to asses cell motility dynamics, and 2) to utilize that tool to advance understanding of mechanobiology and mechanotransduction processes associated with development, wound healing, and disease progression. Therefore, the first portion of this thesis (Chapters 2 and 3) dealt with proof of concept for our newly developed automated cell tracking system, termed ACTIVE (automated contour-based tracking for in vitro environments), while the second portion of this thesis (Chapter 4-7) addressed applying this system in multiple experimental designs to synthesize new knowledge regarding cell-ECM or cell-cell interactions. In Chapter 1, we introduced why cell-ECM interactions are essential for in vivo processes and highlighted the current state of the literature. In Chapter 2, we demonstrated that ACTIVE could achieve greater than 95% segmentation accuracy at multiple cell densities, while improving two-body cell-cell interaction error by up to 43%. In Chapter 3 we showed that ACTIVE could be applied to reveal subtle differences in fibroblast motility atop static wrinkled or static non-wrinkled surfaces at multiple cell densities. In Chapters 4 and 5, we characterized fibroblast motility and intracellular reorganization atop a dynamic shape memory polymer biomaterial, focusing on the role of the Rho-mediated pathway in the observed responses. We then utilized ACTIVE to identify differences in subpopulation dynamics of monoculture versus co-culture endothelial and smooth muscle cells (Chapter 6). In Chapter 7, we applied ACTIVE to investigate E. coli biofilm formation atop poly(dimethylsiloxane) surfaces with varying stiffness and line patterns. Finally, we presented a summary and future work in Chapter 8. Collectively, this work highlights the capabilities of the newly developed ACTIVE tracking system and demonstrates how to synthesize new information about mechanobiology and mechanotransduction processes using dynamic biomaterial platforms

Corneal nerve loss as a surrogate marker for poor pial collaterals in patients with acute ischemic stroke

In patients with acute ischemic stroke, pial collaterals play a key role in limiting neurological disability by maintaining blood flow to ischemic penumbra. We hypothesized that patient with poor pial collaterals will have greater corneal nerve and endothelial cell abnormalities. In a cross-sectional study, 35 patients with acute ischemic stroke secondary to middle cerebral artery (MCA) occlusion with poor (n = 12) and moderate-good (n = 23) pial collaterals and 35 healthy controls underwent corneal confocal microscopy and quantification of corneal nerve and endothelial cell morphology. In patients with MCA stroke, corneal nerve fibre length (CNFL) (P < 0.001), corneal nerve fibre density (CNFD) (P = 0.025) and corneal nerve branch density (CNBD) (P = 0.002) were lower compared to controls. Age, BMI, cholesterol, triglycerides, HDL, LDL, systolic blood pressure, NIHSS and endothelial cell parameters did not differ but mRS was higher (p = 0.023) and CNFL (p = 0.026) and CNBD (p = 0.044) were lower in patients with poor compared to moderate-good collaterals. CNFL and CNBD distinguished subjects with poor from moderate-good pial collaterals with an AUC of 72% (95% CI 53–92%) and 71% (95% CI 53–90%), respectively. Corneal nerve loss is greater in patients with poor compared to moderate-good pial collaterals and may act as a surrogate marker for pial collateral status in patients with ischemic stroke