Robust segmentation of retinal layers in optical coherence tomography images based on a multistage active contour model

Optical Coherence Tomography (OCT) constitutes an imaging technique that is increasing its popularity in the ophthalmology field, since it offers a more complete set of information about the main retinal structures. Hence, it offers detailed information about the eye fundus morphology, allowing the identification of many intraretinal pathological signs. For that reason, over the recent years, Computer-Aided Diagnosis (CAD) systems have spread to work with this image modality and analyze its information. A crucial step for the analysis of the retinal tissues implies the identification and delimitation of the different retinal layers. In this context, we present in this work a fully automatic method for the identification of the main retinal layers that delimits the retinal region. Thus, an active contour-based model was completely adapted and optimized to segment these main retinal boundaries. This fully automatic method uses the information of the horizontal placement of these retinal layers and their relative location over the analyzed images to restrict the search space, considering the presence of shadows that are normally generated by pathological or non-pathological artifacts. The validation process was done using the groundtruth of an expert ophthalmologist analyzing healthy as well as unhealthy patients with different degrees of diabetic retinopathy (without macular edema, with macular edema and with lesions in the photoreceptor layers). Quantitative results are in line with the state of the art of this domain, providing accurate segmentations of the retinal layers even when significative pathological alterations are present in the eye fundus. Therefore, the proposed method is robust enough to be used in complex environments, making it feasible for the ophthalmologists in their routine clinical practice

Statistical comparison of classifiers applied to the interferential tear film lipid layer classification

The tear film lipid layer is heterogeneous among the population. Its classification depends on its thickness and can be done using the interference pattern categories proposed by Guillon. The interference phenomena can be characterised as a colour texture pattern, which can be automatically classified into one of these categories. From a photography of the eye, a region of interest is detected and its low-level features are extracted, generating a feature vector that describes it, to be finally classified in one of the target categories. This paper presents an exhaustive study about the problem at hand using different texture analysis methods in three colour spaces and different machine learning algorithms. All these methods and classifiers have been tested on a dataset composed of 105 images from healthy subjects and the results have been statistically analysed. As a result, the manual process done by experts can be automated with the benefits of being faster and unaffected by subjective factors, with maximum accuracy over 95%

Statistical comparison of classifiers applied to the interferential tear film lipid layer classification

The tear film lipid layer is heterogeneous among the population. Its classification depends on its thickness and can be done using the interference pattern categories proposed by Guillon. The interference phenomena can be characterised as a colour texture pattern, which can be automatically classified into one of these categories. From a photography of the eye, a region of interest is detected and its low-level features are extracted, generating a feature vector that describes it, to be finally classified in one of the target categories. This paper presents an exhaustive study about the problem at hand using different texture analysis methods in three colour spaces and different machine learning algorithms. All these methods and classifiers have been tested on a dataset composed of 105 images from healthy subjects and the results have been statistically analysed. As a result, the manual process done by experts can be automated with the benefits of being faster and unaffected by subjective factors, with maximum accuracy over 95%

Aplicaciones de redes neuronales artificiales en procesado de imágenes

Este libro presenta un intento serio y razonablemente completo de reflexión sobre el estado actual del conocimiento en el campo de la computación neuronal y en su forma de plantear y resolver algunos de estos problemas. Actualmente se entiende por computación neuronal toda computación modular, distribuida, con procesos y/o procesadores autónomos y de grano pequeño, organizados en general en arquitecturas multicapa y en las que parte de la programación se sustituye por el aprendizaje, entendido como el ajuste de parámetros por procedimientos correlacionales o supervisados. En general no se discute el modelo de computación local (sumador seguido de sigmoide), se olvida la biología que dio origen e inspiración al campo y se consideran poco los aspectos metodológicos propios de la ciencia (análisis) y de la ingeniería (síntesis a partir de un conjunto de especificaciones funcionales)

Epithelial‐mesenchymal transition polarization in ovarian carcinomas from patients with high social isolation

● Social isolation has shown robust associations with clinical outcomes in the general population and in patients with cancer. Herein, the authors examined the relationship between social isolation and the molecular characteristics of ovarian tumors. ● The authors investigated the epithelial-mesenchymal transition (EMT), a process whereby tumor cells lose epithelial characteristics and become more embryonic (mesenchymal), thereby enhancing invasiveness. ● Primary analyses demonstrated lower expression of genes previously associated with epithelial differentiation and increased activity of specific EMT-related transcription factors in individuals with high social isolation, indicating increased EMT polarization in these patients. These findings extend the understanding of how socioenvironmental factors may modulate tumor growth

The impact of integrating emotion focused components into psychological therapy: A randomized controlled trial.

This paper presents a randomized controlled trial on assimilative integration, which is aimed at integrating elements from other orientations within one approach to enrich its conceptual and practical repertoire. Elements from Emotion-Focused Therapy (EFT) were integrated into a form of cognitive behavior therapy: Psychological Therapy (PT). In one treatment condition, EFT was added to PT (+EFT) with the intent to enhance therapists' working with emotions. In the other condition, concepts and interventions based on the socialpsychological self-regulation approach were added to PT (+SR). Our assumption was that the +EFT would lead to greater and deeper change, particularly in the follow-up assessments. Patients (n = 104) with anxiety, depression, or adjustment disorders were randomized to the two conditions and treated by 38 therapists who self-selected between the conditions. Primary outcome was symptom severity at 12-month follow-up; secondary outcomes included several measures such as interpersonal problems and quality of life. Variables were assessed at baseline, after 8 and 16 sessions, at posttreatment, and at 6- and 12-month follow-up. Contrary to our hypothesis, no significant between-group effects were found. The findings first suggest the difficulty of topping an already very effective approach to psychotherapy. Alternative interpretations were that the EFT training, while corresponding to regular practice in AI, was not sufficient to make a difference in outcome, or that while profiting from the enhancement of abilities for working with emotions, this was outbalanced by negative effects of difficulties related to the implementation of the new elements