Abstract
The choroid is the middle layer of the eye globe located between the retina and the sclera. It is proven that choroidal thickness is a sign of multiple eye diseases. Optical Coherence Tomography (OCT) is an imaging technique that allows the visualization of tomographic images of near surface tissues like those in the eye globe. The automatic calculation of the choroidal thickness reduces the subjectivity of manual image analysis as well as the time of large scale measurements. In this paper, a method for the automatic estimation of the choroidal thickness from OCT images is presented. The pre-processing of the images is focused on noise reduction, shadow removal and contrast adjustment. The inner and outer boundaries of the choroid are delineated sequentially, resorting to a minimum path algorithm supported by new dedicated cost matrices. The choroidal thickness is given by the distance between the two boundaries. The data are then interpolated and mapped to an infrared image of the eye fundus. The method was evaluated by calculating the error as the distance from the automatically estimated boundaries to the boundaries delineated by an ophthalmologist. The error of the automatic segmentation was low and comparable to the differences between manual segmentations from different ophthalmologists.

Abstract

Abstract
Background: E-cadherin is a cell-cell adhesion molecule and the dysfunction of which is a common feature of more than 70% of all invasive carcinomas, including gastric cancer. Mechanisms behind the loss of E-cadherin function in gastric carcinomas include mutations and silencing at either the DNA or RNA level. Nevertheless, in a high percentage of gastric carcinoma cases displaying E-cadherin dysfunction, the mechanism responsible for E-cadherin dysregulation is unknown. We have previously demonstrated the existence of a bi-directional cross-talk between E-cadherin and two major N-glycan processing enzymes, N-acetylglucosaminyltransferase-III or -V (GnT-III or GnT-V). Methods: In the present study, we have characterized the functional implications of the N-glycans catalyzed by GnT-III and GnT-V on the regulation of E-cadherin biological functions and in the molecular assembly and stability of adherens-junctions in a gastric cancer model. The results were validated in human gastric carcinoma samples. Results: We demonstrated that GnT-III induced a stabilizing effect on E-cadherin at the cell membrane by inducing a delay in the turnover rate of the protein, contributing for the formation of stable and functional adherens-junctions, and further preventing clathrin-dependent E-cadherin endocytosis. Conversely, GnT-V promotes the destabilization of E-cadherin, leading to its mislocalization and unstable adherens-junctions with impairment of cell-cell adhesion. Conclusions: This supports the role of GnT-III on E-cadherin-mediated tumor suppression, and GnT-V on E-cadherin-mediated tumor invasion. General significance: These results contribute to fill the gap of knowledge of those human carcinoma cases harboring E-cadherin dysfunction, opening new insights into the molecular mechanisms underlying E-cadherin regulation in gastric cancer with potential translational clinical applications.

Abstract
The Arteriolar-to-Venular Ratio (AVR) is a well known index for the early diagnosis of diseases such as diabetes, hypertension or cardio-vascular pathologies. This paper presents an automatic approach for the estimation of the AVR in retinal images. The proposed method includes vessel segmentation, vessel caliber estimation, optic disc detection, region of interest determination, artery/vein classification and finally AVR calculation. This method was evaluated using the images of the INSPIRE-AVR dataset. The mean error of the measured AVR values with respect to the reference ones was 0.05, which is identical to the one achieved by a medical expert using a semi-automated system, thus demonstrating the reliability of the herein proposed solution for AVR estimation.

Abstract
The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIREAVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification.

Abstract
Retinal vessel caliber changes are associated with several major diseases, such as diabetes and hypertension. These caliber changes can be evaluated using eye fundus images. However, the clinical assessment is tiresome and prone to errors, motivating the development of automatic methods. An automatic method based on vessel crosssection intensity profile model fitting for the estimation of vessel caliber in retinal images is herein proposed. First, vessels are segmented from the image, vessel centerlines are detected and individual segments are extracted and smoothed. Intensity profiles are extracted perpendicularly to the vessel, and the profile lengths are determined. Then, model fitting is applied to the smoothed profiles. A novel parametric model (DoG-L7) is used, consisting on a Difference-of-Gaussians multiplied by a line which is able to describe profile asymmetry. Finally, the parameters of the best-fit model are used for determining the vessel width through regression using ensembles of bagged regression trees with random sampling of the predictors (random forests). The method is evaluated on the REVIEW public dataset. A precision close to the observers is achieved, outperforming other state-of-the-art methods. The method is robust and reliable for width estimation in images with pathologies and artifacts, with performance independent of the range of diameters.