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

Methods for Analysing Endothelial Cell Shape and Behaviour in Relation to the Focal Nature of Atherosclerosis

The aim of this thesis is to develop automated methods for the analysis of the spatial patterns, and the functional behaviour of endothelial cells, viewed under microscopy, with applications to the understanding of atherosclerosis. Initially, a radial search approach to segmentation was attempted in order to trace the cell and nuclei boundaries using a maximum likelihood algorithm; it was found inadequate to detect the weak cell boundaries present in the available data. A parametric cell shape model was then introduced to fit an equivalent ellipse to the cell boundary by matching phase-invariant orientation fields of the image and a candidate cell shape. This approach succeeded on good quality images, but failed on images with weak cell boundaries. Finally, a support vector machines based method, relying on a rich set of visual features, and a small but high quality training dataset, was found to work well on large numbers of cells even in the presence of strong intensity variations and imaging noise. Using the segmentation results, several standard shear-stress dependent parameters of cell morphology were studied, and evidence for similar behaviour in some cell shape parameters was obtained in in-vivo cells and their nuclei. Nuclear and cell orientations around immature and mature aortas were broadly similar, suggesting that the pattern of flow direction near the wall stayed approximately constant with age. The relation was less strong for the cell and nuclear length-to-width ratios. Two novel shape analysis approaches were attempted to find other properties of cell shape which could be used to annotate or characterise patterns, since a wide variability in cell and nuclear shapes was observed which did not appear to fit the standard parameterisations. Although no firm conclusions can yet be drawn, the work lays the foundation for future studies of cell morphology. To draw inferences about patterns in the functional response of cells to flow, which may play a role in the progression of disease, single-cell analysis was performed using calcium sensitive florescence probes. Calcium transient rates were found to change with flow, but more importantly, local patterns of synchronisation in multi-cellular groups were discernable and appear to change with flow. The patterns suggest a new functional mechanism in flow-mediation of cell-cell calcium signalling

Optical Coherence Tomography Image Analysis of Corneal Tissue

Because of the ubiquitous use of contact lenses, there is considerable interest in better understanding the anatomy of the cornea, the part of the eye in contact with an exterior lens. The recent technology developments in high resolution Optical Coherence Tomography (OCT) devices allows for the in-vivo observation of the structure of the human cornea in 3D and at cellular level resolution. Prolonged wear of contact lenses, inflammations, scarring and diseases can change the structure and physiology of the human cornea. OCT is capable of in-vivo, non-contact, 3D imaging of the human cornea. In this research, novel image processing algorithms were developed to process OCT images of the human cornea, in order to determine the corneal optical scattering and transmission. The algorithms were applied to OCT data sets acquired from multiple subjects before, during and after prolonged (3 hours) wear of soft contact lenses and eye patches, in order to investigate the changes in the corneal scattering associated with hypoxia. Results from this study demonstrate the ability of OCT to measure the optical scattering of corneal tissue and to monitor its changes resulting from external stress (hypoxia)

Optical Coherence Tomography Image Analysis of Corneal Tissue

Because of the ubiquitous use of contact lenses, there is considerable interest in better understanding the anatomy of the cornea, the part of the eye in contact with an exterior lens. The recent technology developments in high resolution Optical Coherence Tomography (OCT) devices allows for the in-vivo observation of the structure of the human cornea in 3D and at cellular level resolution. Prolonged wear of contact lenses, inflammations, scarring and diseases can change the structure and physiology of the human cornea. OCT is capable of in-vivo, non-contact, 3D imaging of the human cornea. In this research, novel image processing algorithms were developed to process OCT images of the human cornea, in order to determine the corneal optical scattering and transmission. The algorithms were applied to OCT data sets acquired from multiple subjects before, during and after prolonged (3 hours) wear of soft contact lenses and eye patches, in order to investigate the changes in the corneal scattering associated with hypoxia. Results from this study demonstrate the ability of OCT to measure the optical scattering of corneal tissue and to monitor its changes resulting from external stress (hypoxia)

Analysis of Attachment, Proliferation and Maturation of Human Embryonic Stem Cell-Derived Retinal Pigment Epithelial Cells on Specific Substrata

Most severe degenerative diseases of retina are often due to malfunctions of retinal pigment epithelium (RPE). Absence of effective treatments has led to development of cell-biomaterial constructs with the aim of creating RPE equivalents for transplantation. Presently, the poor biocompatibility of allologous and xenologous culture substrata in addition with limited amount of source tissue poses the major issues. Well-defined synthetic substrata together with utilization of human embryonic stem cell-derived RPE cells (hESC RPE) are suggested to be potential solutions. In addition, need exists for an effective method to determine the developmental status of cells during the culturing period. This need could be addressed with automated image analysis. The aim of this thesis was to examine the capability of a few specific cell culture substrata to enable attachment, proliferation and maturation of hESC RPE cells. Study included total of 17 xeno-free synthetic materials including 12 BioMaDe Gelators, Purecoat amine and carboxyl, poly(D,L-lactic-co-glycolic acid) (75:25), poly(D,L-lactic acid) (96:4) and poly(L-lactic acid-co-?-caprolactone) (70:30). In addition five materials with natural-origin were studied including chitosan, type I collagen, Matrigel and Substrate X. Type IV collagen was used as control. Growth and maturation were monitored by taking images with specific time intervals. At the end point cellular developmental status was determined by assessing the expression of maturation specific mRNAs by PCR techniques and proteins by immunofluorescence microscopy. In addition, images were used to determine the potential of ImageJ-software as user-friendly image analysis tool for RPE cell analysis. Study demonstrated poor attachment and cell survival on every xeno-free synthetic substrate with cells retaining their initial developmental phase throughout the culturing period, which was supported by gene expression analysis. On the contrary, cells on natural materials attached and proliferated readily. Maturity was further confirmed with immunofluorescence labeling. Image analysis with ImageJ, in turn, confronted many problems mainly arising from heterogeneity of the images. As a conclusion, xeno-free synthetic materials tested in this study show low potential as RPE cell substrata. However, means to enhance their performance are suggested. Despite the good results obtained with natural materials, their ill-defined structure prone to alterations in physiological conditions remains an obstacle for entering clinical experiments. Further experiments should concentrate on combining the strengths of both approaches, that is, incorporation of attachment-related functional groups into well-defined xeno-free synthetic body. In order to increase image homogeneity imaging conditions should be more carefully considered. This way the benefits of automated image analysis could be more effectively exploited. /Kir1

Analysis of Attachment, Proliferation and Maturation of Human Embryonic Stem Cell-Derived Retinal Pigment Epithelial Cells on Specific Substrata

Most severe degenerative diseases of retina are often due to malfunctions of retinal pigment epithelium (RPE). Absence of effective treatments has led to development of cell-biomaterial constructs with the aim of creating RPE equivalents for transplantation. Presently, the poor biocompatibility of allologous and xenologous culture substrata in addition with limited amount of source tissue poses the major issues. Well-defined synthetic substrata together with utilization of human embryonic stem cell-derived RPE cells (hESC RPE) are suggested to be potential solutions. In addition, need exists for an effective method to determine the developmental status of cells during the culturing period. This need could be addressed with automated image analysis. The aim of this thesis was to examine the capability of a few specific cell culture substrata to enable attachment, proliferation and maturation of hESC RPE cells. Study included total of 17 xeno-free synthetic materials including 12 BioMaDe Gelators, Purecoat amine and carboxyl, poly(D,L-lactic-co-glycolic acid) (75:25), poly(D,L-lactic acid) (96:4) and poly(L-lactic acid-co-?-caprolactone) (70:30). In addition five materials with natural-origin were studied including chitosan, type I collagen, Matrigel and Substrate X. Type IV collagen was used as control. Growth and maturation were monitored by taking images with specific time intervals. At the end point cellular developmental status was determined by assessing the expression of maturation specific mRNAs by PCR techniques and proteins by immunofluorescence microscopy. In addition, images were used to determine the potential of ImageJ-software as user-friendly image analysis tool for RPE cell analysis. Study demonstrated poor attachment and cell survival on every xeno-free synthetic substrate with cells retaining their initial developmental phase throughout the culturing period, which was supported by gene expression analysis. On the contrary, cells on natural materials attached and proliferated readily. Maturity was further confirmed with immunofluorescence labeling. Image analysis with ImageJ, in turn, confronted many problems mainly arising from heterogeneity of the images. As a conclusion, xeno-free synthetic materials tested in this study show low potential as RPE cell substrata. However, means to enhance their performance are suggested. Despite the good results obtained with natural materials, their ill-defined structure prone to alterations in physiological conditions remains an obstacle for entering clinical experiments. Further experiments should concentrate on combining the strengths of both approaches, that is, incorporation of attachment-related functional groups into well-defined xeno-free synthetic body. In order to increase image homogeneity imaging conditions should be more carefully considered. This way the benefits of automated image analysis could be more effectively exploited. /Kir1

Visual Impairment and Blindness

Blindness and vision impairment affect at least 2.2 billion people worldwide with most individuals having a preventable vision impairment. The majority of people with vision impairment are older than 50 years, however, vision loss can affect people of all ages. Reduced eyesight can have major and long-lasting effects on all aspects of life, including daily personal activities, interacting with the community, school and work opportunities, and the ability to access public services. This book provides an overview of the effects of blindness and visual impairment in the context of the most common causes of blindness in older adults as well as children, including retinal disorders, cataracts, glaucoma, and macular or corneal degeneration

2019 IMSAloquium: Student Inquiry and Research Program and IMSA Internship Program

Welcome to IMSAloquium 2019! This is IMSA’s 32nd year of leading in educational innovation, the 31st year of the IMSA Student Inquiry and Research (SIR) Program, and the first year of the newly imagined IMSA Internship Program.https://digitalcommons.imsa.edu/archives_sir/1029/thumbnail.jp

Deep learning in ophthalmology: The technical and clinical considerations

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally