Spatial and spatio-temporal statistical analyses of retinal images: a review of methods and applications.

Background: Clinical research and management of retinal diseases greatly depend on the interpretation of retinal images and often longitudinally collected images. Retinal images provide context for spatial data, namely the location of specific pathologies within the retina. Longitudinally collected images can show how clinical events at one point can affect the retina over time. In this review, we aimed to assess statistical approaches to spatial and spatio-temporal data in retinal images. We also review the spatio-temporal modelling approaches used in other medical image types. Methods: We conducted a comprehensive literature review of both spatial or spatio-temporal approaches and non-spatial approaches to the statistical analysis of retinal images. The key methodological and clinical characteristics of published papers were extracted. We also investigated whether clinical variables and spatial correlation were accounted for in the analysis. Results: Thirty-four papers that included retinal imaging data were identified for full-text information extraction. Only 11 (32.4%) papers used spatial or spatio-temporal statistical methods to analyse images, others (23 papers, 67.6%) used non-spatial methods. Twenty-eight (82.4%) papers reported images collected cross-sectionally, while 6 (17.6%) papers reported analyses on images collected longitudinally. In imaging areas outside of ophthalmology, 19 papers were identified with spatio-temporal analysis, and multiple statistical methods were recorded. Conclusions: In future statistical analyses of retinal images, it will be beneficial to clearly define and report the spatial distributions studied, report the spatial correlations, combine imaging data with clinical variables into analysis if available, and clearly state the software or packages used

Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning

Longitudinal imaging is capable of capturing the static ana\-to\-mi\-cal structures and the dynamic changes of the morphology resulting from aging or disease progression. Self-supervised learning allows to learn new representation from available large unlabelled data without any expert knowledge. We propose a deep learning self-supervised approach to model disease progression from longitudinal retinal optical coherence tomography (OCT). Our self-supervised model takes benefit from a generic time-related task, by learning to estimate the time interval between pairs of scans acquired from the same patient. This task is (i) easy to implement, (ii) allows to use irregularly sampled data, (iii) is tolerant to poor registration, and (iv) does not rely on additional annotations. This novel method learns a representation that focuses on progression specific information only, which can be transferred to other types of longitudinal problems. We transfer the learnt representation to a clinically highly relevant task of predicting the onset of an advanced stage of age-related macular degeneration within a given time interval based on a single OCT scan. The boost in prediction accuracy, in comparison to a network learned from scratch or transferred from traditional tasks, demonstrates that our pretrained self-supervised representation learns a clinically meaningful information.Comment: Accepted for publication in the MICCAI 2019 PRIME worksho

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

Novel Approaches for the Treatment of Uveal Melanoma

Il melanoma uveale (MU) è il principale tumore intraoculare nella popolazione adulta. Durante il mio dottorato di ricerca, la mia attività scientifica è stata volta allo studio dei meccanismi coinvolti nella progressione del MU, con lo scopo di identificare nuove strategie terapeutiche dirette contro la componente tumorale e stromale. Il sistema dei fattori di crescita dei fibroblasti (FGF) e dei loro recettori (FGFR) è coinvolto nella crescita e nella progressione tumorale. Inoltre, dati clinici evidenziano come l’elevata espressione di ligandi e/o recettori sia associata ad una prognosi peggiore nei pazienti. In questo lavoro di tesi è stato dimostrato per la prima volta come l’inibizione del sistema FGF/FGFR rappresenti una strategia efficace per colpire le cellule staminali tumorali del MU, coinvolte nella disseminazione metastatica, nella resistenza alle terapie e nell’insorgenza di recidive. Inoltre, durante il mio dottorato di ricerca ho partecipato allo sviluppo di un modello ortotopico di MU nell’embrione di zebrafish. Infine, una parte di questo lavoro di tesi è stata volta ad approfondire i meccanismi di escape immunologico messi in atto dal MU per sfuggire al controllo del sistema immunitario. In questo contesto, i linfociti Natural Killer (NK) sono una popolazione di cellule dell’immunità che riveste un ruolo fondamentale nell’immunosorveglianza nei confronti delle cellule tumorali. I nostri dati preliminari dimostrano che il MU esprime elevati livelli del fattore immunosoppressivo TGFb e che è in grado “riprogrammare” i linfociti NK verso un fenotipo pro-tumorale, detto decidual-like, ponendo le basi per ulteriori studiUveal melanoma (UM) is a very aggressive tumor, and it represents the most common primary intraocular malignancy in the adult population. While primary tumors are successfully treated in 90% of cases, almost 50% of patients ultimately develops metastasis, with a median survival after diagnosis spanning from 6 to 12 months. Therefore, effective pharmacological therapies are eagerly required. In this frame, during my PhD I have focused on gaining a better understanding on the mechanisms sustaining tumor progression in the attempt to identify novel therapeutic strategies. In this thesis, I have illustrated our results on alternative approaches aimed at hampering both tumor cells as well as the stromal component. The Fibroblast Growth Factor (FGF)/FGF Receptor (FGFR) system exerts a very important role in UM. Indeed, both clinical and experimental evidence demonstrates the presence of an autocrine FGF/FGFR activation loop, with alterations in the expression of ligands and receptors resulting in a poorer prognosis in patients. In this context, we have previously demonstrated the efficacy of inhibiting the FGF/FGFR system using the pan FGF-trap NSC12 as a strategy to reduce cell proliferation, migration, and survival of UM cell lines. Additionally, FGF-mediated signaling is also involved in the maintenance of Cancer Stem-like Cells (CSCs), a subpopulation of tumor cells responsible for tumorigenesis, metastatic dissemination, therapy resistance, and recurrence. Therefore, eliminating CSCs is a crucial step to achieve a complete tumor eradication. On this premise, we have demonstrated for the first time that the inhibition of the FGF/FGFR system is an effective strategy to hamper the stem-like component due to the enhanced sensitivity of CSCs to FGF-deprivation. In this frame, we have also established an orthotopic model of UM in the zebrafish embryo as a tool for in vivo drug screening. By engrafting tumor cells in proximity to the developing choroidal vasculature of the eye, our model closely mimics the microenvironment in which tumors originate. Additionally, we have developed a reliable and accurate method for assessing xenograft tumor growth by exploiting the bioluminescent signal of tumor cells transduced with firefly luciferase. The advent of immune therapy strategies has failed to improve the clinical management of UM, due to the exploitation of immune escape strategies that are still largely unclear. In this context, Natural Killer (NK) lymphocytes are important regulators of cancer immunosurveillance and their activity is finely controlled by the expression of specific activating and inhibitory receptors that allow them to discriminate and eliminate malignant cells. However, the presence of a pro-tumor and pro-angiogenic subpopulation of decidual-like NK lymphocytes has been recently described in various tumor types. These cells are characterized by the production of pro-angiogenic/pro-inflammatory mediators as well as by an impairment of their cytotoxic functions. On this premise, we have investigated whether decidual-like polarization of NK lymphocytes could be involved in UM, as a process sustaining tumor progression as well as the formation of metastatic lesions. Our data demonstrates that the conditioned media from UM cell can shift NK lymphocytes towards a decidual-like state, characterized by reduced levels of activating receptors and by an impaired cytotoxic activity. These data, together with the evidence that UM cells express the immunosuppressive cytokine TGFb, support the hypothesis that soluble factors produced by cancer cells and accumulated within the tumor microenvironment could favor UM immune escape. Our results set the basis for further studies on the role played by UM-derived TGFb in reprogramming NK lymphocytes and they hint at TGFb as a potential target for the treatment of UM

Methods for automated analysis of macular OCT data

Optical coherence tomography (OCT) is fast becoming one of the most important modalities for imaging the eye. It provides high resolution, cross-sectional images of the retina in three dimensions, distinctly showing its many layers. These layers are critical for normal eye function, and vision loss may occur when they are altered by disease. Specifically, the thickness of individual layers can change over time, thereby making the ability to accurately measure these thicknesses an important part of learning about how different diseases affect the eye. Since manual segmentation of the layers in OCT data is time consuming and tedious, automated methods are necessary to extract layer thicknesses. While a standard set of tools exist on the scanners to automatically segment the retina, the output is often limited, providing measurements restricted to only a few layers. Analysis of longitudinal data is also limited, with scans from the same subject often processed independently and registered using only a single landmark at the fovea. Quantification of other changes in the retina, including the accumulation of fluid, are also generally unavailable using the built-in software. In this thesis, we present four contributions for automatically processing OCT data, specifically for data acquired from the macular region of the retina. First, we present a layer segmentation algorithm to robustly segment the eight visible layers of the retina. Our approach combines the use of a random forest (RF) classifier, which produces boundary probabilities, with a boundary refinement algorithm to find surfaces maximizing the RF probabilities. Second, we present a pair of methods for processing longitudinal data from individual subjects: one combining registration and motion correction, and one for simultaneously segmenting the layers across all scans. Third, we develop a method for segmentation of microcystic macular edema, which appear as small, fluid-filled, cystoid spaces within the retina. Our approach again uses an RF classifier to produce a robust segmentation. Finally, we present the development of macular flatspace (MFS), a computational domain used to put data from different subjects in a common coordinate system where each layer appears flat, thereby simplifying any automated processing. We present two applications of MFS: inhomogeneity correction to normalize the intensities within each layer, and layer segmentation by adapting and simplifying a graph formulation used previously

Molecular Imaging

The present book gives an exceptional overview of molecular imaging. Practical approach represents the red thread through the whole book, covering at the same time detailed background information that goes very deep into molecular as well as cellular level. Ideas how molecular imaging will develop in the near future present a special delicacy. This should be of special interest as the contributors are members of leading research groups from all over the world