Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks

Glaucoma is one of the leading causes of blindness worldwide and Optical Coherence Tomography (OCT) is the quintessential imaging technique for its detection. Unlike most of the state-of-the-art studies focused on glaucoma detection, in this paper, we propose, for the first time, a novel framework for glaucoma grading using raw circumpapillary B-scans. In particular, we set out a new OCT-based hybrid network which combines hand-driven and deep learning algorithms. An OCT-specific descriptor is proposed to extract hand-crafted features related to the retinal nerve fibre layer (RNFL). In parallel, an innovative CNN is developed using skip-connections to include tailored residual and attention modules to refine the automatic features of the latent space. The proposed architecture is used as a backbone to conduct a novel few-shot learning based on static and dynamic prototypical networks. The k-shot paradigm is redefined giving rise to a supervised end-to-end system which provides substantial improvements discriminating between healthy, early and advanced glaucoma samples. The training and evaluation processes of the dynamic prototypical network are addressed from two fused databases acquired via Heidelberg Spectralis system. Validation and testing results reach a categorical accuracy of 0.9459 and 0.8788 for glaucoma grading, respectively. Besides, the high performance reported by the proposed model for glaucoma detection deserves a special mention. The findings from the class activation maps are directly in line with the clinicians' opinion since the heatmaps pointed out the RNFL as the most relevant structure for glaucoma diagnosis

Glaucoma Detection from Raw SD-OCT Volumes: a Novel Approach Focused on Spatial Dependencies

[EN] Background and objective:Glaucoma is the leading cause of blindness worldwide. Many studies based on fundus image and optical coherence tomography (OCT) imaging have been developed in the literature to help ophthalmologists through artificial-intelligence techniques. Currently, 3D spectral-domain optical coherence tomography (SD-OCT) samples have become more important since they could enclose promising information for glaucoma detection. To analyse the hidden knowledge of the 3D scans for glaucoma detection, we have proposed, for the first time, a deep-learning methodology based on leveraging the spatial dependencies of the features extracted from the B-scans. Methods:The experiments were performed on a database composed of 176 healthy and 144 glaucomatous SD-OCT volumes centred on the optic nerve head (ONH). The proposed methodology consists of two well-differentiated training stages: a slide-level feature extractor and a volume-based predictive model. The slide-level discriminator is characterised by two new, residual and attention, convolutional modules which are combined via skip-connections with other fine-tuned architectures. Regarding the second stage, we first carried out a data-volume conditioning before extracting the features from the slides of the SD-OCT volumes. Then, Long Short-Term Memory (LSTM) networks were used to combine the recurrent dependencies embedded in the latent space to provide a holistic feature vector, which was generated by the proposed sequential-weighting module (SWM). Results:The feature extractor reports AUC values higher than 0.93 both in the primary and external test sets. Otherwise, the proposed end-to-end system based on a combination of CNN and LSTM networks achieves an AUC of 0.8847 in the prediction stage, which outperforms other state-of-the-art approaches intended for glaucoma detection. Additionally, Class Activation Maps (CAMs) were computed to highlight the most interesting regions per B-scan when discerning between healthy and glaucomatous eyes from raw SD-OCT volumes. Conclusions:The proposed model is able to extract the features from the B-scans of the volumes and combine the information of the latent space to perform a volume-level glaucoma prediction. Our model, which combines residual and attention blocks with a sequential weighting module to refine the LSTM outputs, surpass the results achieved from current state-of-the-art methods focused on 3D deep-learning architectures.The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used here.This work has been funded by GALAHAD project [H2020-ICT-2016-2017, 732613], SICAP project (DPI2016-77869-C2-1-R) and GVA through project PROMETEO/2019/109. The work of Gabriel García has been supported by the State Research Spanish Agency PTA2017-14610-I.García-Pardo, JG.; Colomer, A.; Naranjo Ornedo, V. (2021). Glaucoma Detection from Raw SD-OCT Volumes: a Novel Approach Focused on Spatial Dependencies. Computer Methods and Programs in Biomedicine. 200:1-16. https://doi.org/10.1016/j.cmpb.2020.105855S116200Weinreb, R. N., & Khaw, P. T. (2004). Primary open-angle glaucoma. The Lancet, 363(9422), 1711-1720. doi:10.1016/s0140-6736(04)16257-0Jonas, J. B., Aung, T., Bourne, R. R., Bron, A. M., Ritch, R., & Panda-Jonas, S. (2018). Glaucoma – Authors’ reply. The Lancet, 391(10122), 740. doi:10.1016/s0140-6736(18)30305-2Tham, Y.-C., Li, X., Wong, T. Y., Quigley, H. A., Aung, T., & Cheng, C.-Y. (2014). Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040. Ophthalmology, 121(11), 2081-2090. doi:10.1016/j.ophtha.2014.05.013Huang, D., Swanson, E. A., Lin, C. P., Schuman, J. S., Stinson, W. G., Chang, W., … Fujimoto, J. G. 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Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomedical Optics Express, 8(5), 2732. doi:10.1364/boe.8.002732Pekala, M., Joshi, N., Liu, T. Y. A., Bressler, N. M., DeBuc, D. C., & Burlina, P. (2019). Deep learning based retinal OCT segmentation. Computers in Biology and Medicine, 114, 103445. doi:10.1016/j.compbiomed.2019.103445Barella, K. A., Costa, V. P., Gonçalves Vidotti, V., Silva, F. R., Dias, M., & Gomi, E. S. (2013). Glaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCT. Journal of Ophthalmology, 2013, 1-7. doi:10.1155/2013/789129Vidotti, V. G., Costa, V. P., Silva, F. R., Resende, G. M., Cremasco, F., Dias, M., & Gomi, E. S. (2013). Sensitivity and Specificity of Machine Learning Classifiers and Spectral Domain OCT for the Diagnosis of Glaucoma. European Journal of Ophthalmology, 23(1), 61-69. doi:10.5301/ejo.5000183Xu, J., Ishikawa, H., Wollstein, G., Bilonick, R. A., Folio, L. S., Nadler, Z., … Schuman, J. S. (2013). Three-Dimensional Spectral-Domain Optical Coherence Tomography Data Analysis for Glaucoma Detection. PLoS ONE, 8(2), e55476. doi:10.1371/journal.pone.0055476Maetschke, S., Antony, B., Ishikawa, H., Wollstein, G., Schuman, J., & Garnavi, R. (2019). A feature agnostic approach for glaucoma detection in OCT volumes. PLOS ONE, 14(7), e0219126. doi:10.1371/journal.pone.0219126Ran, A. R., Cheung, C. Y., Wang, X., Chen, H., Luo, L., Chan, P. P., … Tham, C. C. (2019). Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis. The Lancet Digital Health, 1(4), e172-e182. doi:10.1016/s2589-7500(19)30085-8De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., … Ronneberger, O. (2018). 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Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography

Tesis por compendio[ES] Esta tesis presenta soluciones de vanguardia basadas en algoritmos de computer vision (CV) y machine learning (ML) para ayudar a los expertos en el diagnóstico clínico. Se centra en dos áreas relevantes en el campo de la imagen médica: la patología digital y la oftalmología. Este trabajo propone diferentes paradigmas de machine learning y deep learning para abordar diversos escenarios de supervisión en el estudio del cáncer de próstata, el cáncer de vejiga y el glaucoma. En particular, se consideran métodos supervisados convencionales para segmentar y clasificar estructuras específicas de la próstata en imágenes histológicas digitalizadas. Para el reconocimiento de patrones específicos de la vejiga, se llevan a cabo enfoques totalmente no supervisados basados en técnicas de deep-clustering. Con respecto a la detección del glaucoma, se aplican algoritmos de memoria a corto plazo (LSTMs) que permiten llevar a cabo un aprendizaje recurrente a partir de volúmenes de tomografía por coherencia óptica en el dominio espectral (SD-OCT). Finalmente, se propone el uso de redes neuronales prototípicas (PNN) en un marco de few-shot learning para determinar el nivel de gravedad del glaucoma a partir de imágenes OCT circumpapilares. Los métodos de inteligencia artificial (IA) que se detallan en esta tesis proporcionan una valiosa herramienta de ayuda al diagnóstico por imagen, ya sea para el diagnóstico histológico del cáncer de próstata y vejiga o para la evaluación del glaucoma a partir de datos de OCT.[CA] Aquesta tesi presenta solucions d'avantguarda basades en algorismes de *computer *vision (CV) i *machine *learning (ML) per a ajudar als experts en el diagnòstic clínic. Se centra en dues àrees rellevants en el camp de la imatge mèdica: la patologia digital i l'oftalmologia. Aquest treball proposa diferents paradigmes de *machine *learning i *deep *learning per a abordar diversos escenaris de supervisió en l'estudi del càncer de pròstata, el càncer de bufeta i el glaucoma. En particular, es consideren mètodes supervisats convencionals per a segmentar i classificar estructures específiques de la pròstata en imatges histològiques digitalitzades. Per al reconeixement de patrons específics de la bufeta, es duen a terme enfocaments totalment no supervisats basats en tècniques de *deep-*clustering. Respecte a la detecció del glaucoma, s'apliquen algorismes de memòria a curt termini (*LSTMs) que permeten dur a terme un aprenentatge recurrent a partir de volums de tomografia per coherència òptica en el domini espectral (SD-*OCT). Finalment, es proposa l'ús de xarxes neuronals *prototípicas (*PNN) en un marc de *few-*shot *learning per a determinar el nivell de gravetat del glaucoma a partir d'imatges *OCT *circumpapilares. Els mètodes d'intel·ligència artificial (*IA) que es detallen en aquesta tesi proporcionen una valuosa eina d'ajuda al diagnòstic per imatge, ja siga per al diagnòstic histològic del càncer de pròstata i bufeta o per a l'avaluació del glaucoma a partir de dades d'OCT.[EN] This thesis presents cutting-edge solutions based on computer vision (CV) and machine learning (ML) algorithms to assist experts in clinical diagnosis. It focuses on two relevant areas at the forefront of medical imaging: digital pathology and ophthalmology. This work proposes different machine learning and deep learning paradigms to address various supervisory scenarios in the study of prostate cancer, bladder cancer and glaucoma. In particular, conventional supervised methods are considered for segmenting and classifying prostate-specific structures in digitised histological images. For bladder-specific pattern recognition, fully unsupervised approaches based on deep-clustering techniques are carried out. Regarding glaucoma detection, long-short term memory algorithms (LSTMs) are applied to perform recurrent learning from spectral-domain optical coherence tomography (SD-OCT) volumes. Finally, the use of prototypical neural networks (PNNs) in a few-shot learning framework is proposed to determine the severity level of glaucoma from circumpapillary OCT images. The artificial intelligence (AI) methods detailed in this thesis provide a valuable tool to aid diagnostic imaging, whether for the histological diagnosis of prostate and bladder cancer or glaucoma assessment from OCT data.García Pardo, JG. (2022). Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182400Compendi

Macular Pigment and Diabetes Mellitus

The macula is a specialised part of the retina responsible for detailed central and colour vision. The carotenoids lutein, zeaxanthin and meso-zeaxanthin are uniquely concentrated in the inner and central layers of the primate macula, where they are collectively known as macular pigment (MP). Macular pigment confers potent antioxidant and anti-inflammatory effects in the eye. Many studies have suggested that these carotenoids are lower in age-related macular degeneration (AMD) and that increased levels of MP may confer protection against AMD, especially the late form of the disease. Research is now beginning to focus on MP optical density (MPOD) and carotenoid intake in diabetes mellitus, a condition which similar to AMD, is known to cause oxidative damage and inflammation in the retina. Methods The optical density of MP was measured in a group of participants with diabetes (Type 1 and Type 2) and findings compared with normal healthy controls. A comprehensive review was performed to investigate the putative causal mechanisms and metabolic perturbations associated with lower MP in diabetes. Surrogate biomarkers for the prediction of low MP in participants with Type 2 diabetes and older adults free of ocular pathology, including clinical [blood pressure], plasma [lipoproteins, inflammatory markers] and anthropometric [waist (cm), hip (cm), height (cm), weight (kg)] parameters, were investigated and identified as part of a large randomly selected sample from the Republic of Ireland (as part of The Irish Longitudinal Study on Ageing [TILDA]). 2 Results The optical density of MP was lower among Type 2 diabetes subjects (0.33 ± 0.21) compared with Type 1 subjects (0.49 ± 0.23) and normal controls (0.48 ± 0.35) (p= 0.01). A comprehensive review of MP and diabetes, Type 2 diabetes, in particular, revealed that MP may become depleted through at least four possible causal mechanisms in this condition including overweight/obesity, dyslipidaemia, oxidative stress and inflammation. Research performed on the TILDA cohort confirmed that participants with Type 2 diabetes had significantly lower MPOD compared with non diabetic controls (p=0.047). In-depth analysis on this Type 2 diabetes cohort revealed that MP was significantly lower in diabetes participants who were deficient in plasma vitamin D (/L) (p=0.006); who had a raised triglyceride (TG) over high density lipoprotein (HDL) ratio (TG/HDL) [\u3e1.74 mmol/L; p=0.039]; who had hypertension (p=0.043); who were current smokers (p=0.022); or who had cataracts (p=0.049). Among older adults who were free of ocular pathology (i.e. AMD, glaucoma, diabetes, pre-diabetes), MPOD was significantly lower among participants with an elevated waist circumference (WC) (p=0.034), those who had low plasma HDL (p=0.038), those with a raised plasma TG/HDL ratio (p=0.003) and those with a raised total cholesterol (TC) over HDL ratio (TC/HDL) (p=0.030). Conclusion Overall, our findings suggest that individuals with Type 2 diabetes have lower MP relative to healthy controls. The metabolic correlates associated with Type 2 diabetes, in particular, i.e. oxidative stress, inflammation, overweight/obesity and dyslipidaemia, may have important implications for MPOD in the retina. Surrogate biomarkers associated with lower MP in Type 2 diabetes include low plasma levels of 3 vitamin D (25(OH) D), dyslipidaemia (i.e. raised TG/HDL ratio), hypertension, cataracts and smoking. While an altered lipoprotein profile (i.e. low HDL, raised TG/HDL ratio, raised TC/HDL ratio), may affect the transport, uptake, and stabilisation of carotenoids in the retina of older adults free of ocular pathology, it appears that WC is a more robust predictor of lower MPOD in this patient cohort. However, its effect size appears to be small and therefore its clinical applicability is questionable

Genotype-Phenotype Correlations in Fuchs Corneal Endothelial Dystrophy

Fuchs endothelial corneal dystrophy (FECD) is the most common form of corneal dystrophy and the leading indication of corneal transplant surgery worldwide. Over the past decade, greater insight into the genetic changes causative of FECD has provided clinicians with an opportunity to improve our understanding of this dystrophy and to explore new potential therapeutic avenues. In this thesis, I investigate the genotype-phenotype correlations identified in a cohort of 342 FECD patients recruited from Moorfields Eye Hospital. By first studying the CTG trinucleotide repeat expansion within the TCF4 gene (termed CTG18.1), the causal variant in the majority of FECD patients, I have shown the existence of significant demographic differences between FECD patients who carry this mutation (ExpPos) and those who do not (ExpNeg). I have also demonstrated a significant correlation between repeat expansion size and disease severity in ExpPos subjects, as longer repeat length was associated with younger age at transplantation. I then carried out phenotype analysis on ExpNeg patients who were identified to harbour other FECDassociated variants via whole exome sequencing. The findings within this subgroup support the classification of corneal decompensation secondary to mutations in the COL8A2 gene as a clinical entity distinct from FECD, in view of significant differences in both disease onset and severity. Investigation of the FECD cohort further highlighted the rare concurrent presentation of keratoconus in this population. Although we found no definitive genetic link between the two diseases, this study highlights the clinical challenges that arise in the diagnosis and management of patients presenting with the dual pathology. Finally, to translate the advances made in the field of molecular genetics into improved treatment options for FECD patients, I discuss the design of a Phase 1 clinical trial to evaluate the therapeutic potential and safety of an antisense oligonucleotide therapy targeting ExpPos FECD

Early detection of diabetic macular oedema

Background Diabetic retinopathy (DR) is the second leading cause of visual loss in working-age adults in the United Kingdom (UK) after inherited eye disease, and is asymptomatic in its early stages. Visual loss from DR is commonly due to diabetic macular oedema (DMO) which current screening methods cannot detect directly. The handheld radial shape discrimination (hRSD) test, has been approved by the US Food and Drug Administration (FDA) as a means of detecting metamorphopsia, and therefore maculopathy. There is also emerging evidence that DR is a neurodegenerative disease resulting in thinning of the ganglion cell complex detected by optical coherence tomography (OCT) in early DR. This thesis describes studies of people with diabetes (PWD) and healthy controls (HC) investigating two emerging approaches, namely hRSD and OCT in the early detection of DMO. Methods Macular function was measured using hRSD, distance and near visual acuity (VA) and macular structure was assessed using Heidelberg Spectralis OCT. Retinal layers segmentation and mean thicknesses were measured across all Early Treatment Diabetic Retinopathy Study (ETDRS) subfields using the Heidelberg auto-segmentation software with manual adjustment as needed. One eye from each participant was randomly selected for analysis. Results 292 PWD (mean±SD 54±14 years, 175 males) referred from the local screening programme to hospital clinics as being at risk of DMO were recruited. 229 healthy participants (age 44±18 years; 94 males) were also recruited, of whom 50 (55±14 years, 26 males) were used as age-matched controls for the PWD. Compared to HC, hRSD performance and distance VA were progressively worse in PWD with no or minimal DR, (hRSD logMAR: HC -0.77±0.11, no DR -0.68±0.18, minimal DR -0.61±0.25, ANOVA p<0.001; distance VA logMAR: HC -0.08±0.12, no DR 0.03±0.15, minimal DR 0.06±0.16, ANOVA p<0.001). Compared to HC there was a reduction in full retinal thickness across most subfields in PWD with no or minimal DR. This reduction was driven by thinning in the outer nuclear layer (ONL) in the central subfield (CSF), ganglion cell layer (GCL) and inner plexiform layer (IPL) in the inner subfields and retinal nerve fibre layer (RNFL) in the outer subfields compared to HC. In the outer subfields, there was also thinning in the retinal pigment epithelium (RPE) in PWD with no DR and thinning in the GCL and IPL in PWD with minimal DR. Longitudinal data were available for 159 PWD (54±15 years, 97 males) who attended for a second visit after 191±86 days. In PWD with no or minimal DR, there was a significant decrease in GCL (visit 1 37.73±3.56µm, visit 2 37.27±3.84µm, t=2.523, p=0.020), IPL (visit 1 31.98±2.48µm, visit 2 31.61±2.69µm, t=2.517, p=0.020) and inner nuclear layer (INL) (visit 1 33.89±1.92µm, visit 2 32.96±1.11µm, t=3.129, p=0.005) between visits. Conclusions Functional and structural changes are detectable in the early pathogenesis of DR, consistent with neuroretinal thinning developing before microvascular abnormalities. Functional changes detected by the hRSD test in PWD with early DR have not been previously demonstrated. Findings from the Early Detection of Diabetic Macular Oedema (EDDMO) study add further support to the concept of pre-clinical retinopathy

The association between macular pigment optical density and glare recovery time with selected macular degeneration and ocular vascular perfusion risk factors

Age-related macular degeneration (AMD) is the most common cause of severe visual impairment affecting older adults in the developed world. The pathogenesis of AMD is not fully understood. This study sought to investigate the association between macular pigment optical density (MPOD) and glare recovery time (GRT) with a selection of other confirmed and putative AMD risk factors (RF): age, gender, body mass index (BMI), calculated percentage body fat (%BF), iris colour, family history (FH) of AMD, and ocular vascular perfusion (OVP) RF: migraine, Raynaud's phenomenon (Rph) and vascular dysregulation (VDys). Interocular comparison was assessed for MPOD and GRT. The effect of ocular dominance on MPOD and GRT, and GRT repeatability was also examined. The use of GRT as a surrogate measure for MPOD was assessed. In this healthy, mixed-gender, White population no significant association was found between MPOD measured by heterochromatic flicker photometry (HFP) at 0.5° eccentricity and any AMD or OVP RF assessed by this study. No significant interocular difference in MPOD was found. No significant association was found between MPOD and ocular dominance. GRT after 30-second duration bleach using the direct ophthalmoscope was significantly and positively associated with age. No significant association was found for any other AMD or OVP RF examined, after correction for age. No significant interocular difference was found. No significant association was found with ocular dominance. GRT intra-session repeatability was good and inter-session repeatability was moderate. This method of GRT was not found to be a good surrogate measure for MPOD. This study generated three new theories: the possible association between the OVP RF migraine, Rph and VDys and AMD risk, the Müller cell (Mc) / neuroglial cell hypothesis for macular pigment, and the retinal theory for Meares-Irlen syndrome (MIS) also known as Visual Stress

RETINAL IMAGING IN CEREBROVASCULAR DISEASE AND DEMENTIA

Ph.DDOCTOR OF PHILOSOPH

Quantifying ocular inflammation in uveitis using optical coherence tomography

Inflammation is the key underlying physiological process in uveitis. It drives the onset of acute flares, causes permanent structural damage and can result in sight-threatening complications. Being able to accurately detect and measure changes in inflammatory activity is crucial for managing uveitic flares and rationalising therapeutic decisions. Unfortunately, many of the current methods for quantifying inflammation are imperfect, due to the fact that they are based on subjective and unreliable clinician estimates. In this thesis, I evaluated the potential for imaging-based technologies such as optical coherence tomography (OCT) to measure key markers of intraocular inflammation in uveitis. Whilst several key markers of inflammation are recognised, this thesis focuses on those with an existing clinical standard, which can be used as a comparator or reference test (anterior chamber cells, anterior chamber flare and vitreous haze). I conducted a series of systematic reviews evaluating potential instrument-based techniques for measuring anterior chamber cells, anterior chamber flare and vitreous inflammation, respectively. These identified OCT and laser flare photometry as potential instruments for measuring anterior chamber cell and flare, and OCT and retinal photography for measuring vitreous inflammation. However, the interpretation of results in each review was limited by relatively few studies and the inclusion of highly heterogenous uveitic patient populations, varying severities of disease, and lack of a standardised image acquisition protocol. Second, in the prospective study, OCTAVE (OCT-assisted vitreous evaluation), I found that our custom OCT-based vitreous analysis technique (EQUIP) demonstrated good repeatability in healthy and uveitic eyes, was able to detect vitreous inflammation and was associated with the current clinical vitreous haze grading. The EQUIP measurement was able to predict visual acuity whereas the current standard method (clinician grading 3 using the National Eye Institutevitreous haze scale) could not. Whilst these results were encouraging, there remains substantial overlap in the OCT measurement between NEI vitreous haze grades. It is not clear whether this is due to poor signal-to-noise ratio of the OCT technique, or a sign of poor reliability of the comparator (clinician-based grading using the NEI vitreous haze scale). Further investigation through longitudinal studies may be able to answer this question. In summary, OCT has demonstrated potential for quantifying inflammation for multiple key measures in uveitis. However, a key limitation for the validation of all instrument-based measures has been the lack of a reliable reference test

Harmonic Analysis and Machine Learning

This dissertation considers data representations that lie at the interesection of harmonic analysis and neural networks. The unifying theme of this work is the goal for robust and reliable machine learning. Our specific contributions include a new variant of scattering transforms based on a Haar-type directional wavelet, a new study of deep neural network instability in the context of remote sensing problems, and new empirical studies of biomedical applications of neural networks