Caracterización del Edema Macular Diabético mediante análisis automático de Tomografías de Coherencia Óptica

Programa Oficial de Doctorado en Computación. 5009V01[Abstract] Diabetic Macular Edema (DME) is one of the most important complications of diabetes and a leading cause of preventable blindness in the developed countries. Among the di erent image modalities, Optical Coherence Tomography (OCT) is a non-invasive, cross-sectional and high-resolution imaging technique that is commonly used for the analysis and interpretation of many retinal structures and ocular disorders. In this way, the development of Computer-Aided Diagnosis (CAD) systems has become relevant over the recent years, facilitating and simplifying the work of the clinical specialists in many relevant diagnostic processes, replacing manual procedures that are tedious and highly time-consuming. This thesis proposes a complete methodology for the identi cation and characterization of DMEs using OCT images. To do so, the system combines and exploits di erent clinical knowledge with image processing and machine learning strategies. This automatic system is able to identify and characterize the main retinal structures and several pathological conditions that are associated with the DME disease, following the clinical classi cation of reference in the ophthalmological eld. Despite the complexity and heterogeneity of this relevant ocular pathology, the proposed system achieved satisfactory results, proving to be robust enough to be used in the daily clinical practice, helping the clinicians to produce a more accurate diagnosis and indicate adequate treatments[Resumen] El Edema Macular Diabético (EMD) es una de las complicaciones más importantes de la diabetes y una de las principales causas de ceguera prevenible en los países desarrollados. Entre las diferentes modalidades de imagen, la Tomografía de Coherencia Óptica (TCO) es una técnica de imagen no invasiva, transversal y de alta resolución que se usa comúnmente para el análisis e interpretación de múltiples estructuras retinianas y trastornos oculares. De esta manera, el desarrollo de los sistemas de Diagnóstico Asistido por Ordenador (DAO) se ha vuelto relevante en los últimos años, facilitando y simplificando el trabajo de los especialistas clínicos en muchos procesos diagnósticos relevantes, reemplazando procedimientos manuales que son tediosos y requieren mucho tiempo. Esta tesis propone una metodología completa para la identificación y caracterización de EMDs utilizando imágenes TCO. Para ello, el sistema desarrollado combina y explota diferentes conocimientos clínicos con estrategias de procesamiento de imágenes y aprendizaje automático. Este sistema automático es capaz de identificar y caracterizar las principales estructuras retinianas y diferentes afecciones patológicas asociadas con el EMD, siguiendo la clasificación clínica de referencia en el campo oftalmológico. A pesar de la complejidad de esta relevante patología ocular, el sistema propuesto logró resultados satisfactorios, demostrando ser lo sufi cientemente robusto como para ser usado en la práctica clínica diaria, ayudando a los médicos a producir diagnósticos más precisos y tratamientos más adecuados.[Resumo] O Edema Macular Diabético ( EMD) é unha das complicacións máis importantes da diabetes e unha das principais causas de cegueira prevenible nos países desenvoltos. Entre as diferentes modalidades de imaxe, a Tomografía de Coherencia Óptica ( TCO) é unha técnica de imaxe non invasiva, transversal e de alta resolución que se usa comunmente para a análise e interpretación de múltiples estruturas retinianas e trastornos oculares. Desta maneira, o desenvolvemento dos sistemas de Diagnóstico Asistido por Computador ( DAO) volveuse relevante nos últimos anos, facilitando e simplificando o traballo dos especialistas clínicos en moitos procesos diagnósticos relevantes, substituíndo procedementos manuais que son tediosos e requiren moito tempo. Esta tese propón unha metodoloxía completa para a identificación e caracterización de EMDs utilizando imaxes TCO. Para iso, o sistema desenvolto combina e explota diferentes coñecementos clínicos con estratexias de procesamento de imaxes e aprendizaxe automático. Este sistema automático é capaz de identificar e caracterizar as principais estruturas retinianas e diferentes afeccións patolóxicas asociadas co EMD, seguindo a clasificación clínica de referencia no campo oftalmolóxico. A pesar da complexidade desta relevante patoloxía ocular, o sistema proposto logrou resultados satisfactorios, demostrando ser o sufi cientemente robusto como para ser usado na práctica clínica diaria, axudando aos médicos para producir diagnósticos máis precisos e tratamentos máis adecuados

Fully Automatic Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease. In this work, we propose 4 fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases. Given the similarity between the pathological impact in the lungs between Covid-19 and pneumonia, mainly during the initial stages of both lung diseases, we performed an exhaustive study of differentiation considering different pathological scenarios. To address these classification tasks, we evaluated 6 representative state-of-the-art deep network architectures on 3 different public datasets: (I) Chest X-ray dataset of the Radiological Society of North America (RSNA); (II) Covid-19 Image Data Collection; (III) SIRM dataset of the Italian Society of Medical Radiology. To validate the designed approaches, several representative experiments were performed using 6,070 chest X-ray radiographs. In general, satisfactory results were obtained from the designed approaches, reaching a global accuracy values of 0.9706 ± 0.0044, 0.9839 ± 0.0102, 0.9744 ± 0.0104 and 0.9744 ± 0.0104, respectively, thus helping the work of clinicians in the diagnosis and consequently in the early treatment of this relevant pandemic pathology.This research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research project with reference PID2019-108435RB-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, Spain through the postdoctoral grant contract ref. ED481B-2021-059; and Grupos de Referencia Competitiva, Spain, grant ref. ED431C 2020/24; Axencia Galega de Innovación (GAIN), Xunta de Galicia, Spain, grant ref. IN845D 2020/38; CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia, Spain”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014–2020, Spain, and the remaining 20% by “Secretaría Xeral de Universidades, Spain ” (Grant ED431G 2019/01). Funding for open access charge: Universidade da Coruña/CISUGXunta de Galicia; ED481B-2021-059Xunta de Galicia; ED431C 2020/24Xunta de Galicia; IN845D 2020/38Xunta de Galicia; ED431G 2019/0

Otimização das Vendas de Produtos da Panificação

O emprego da estatística multivariada da análise de componentes principais, também conhecida como transformada de Hotelling ou transformada de Karhunen-Loève, na redução da dimensão dos dados observados sem perda substancial de informação, permite uma análise do comportamento das vendas de produtos da indústria da panificação, através de grupos específicos de produtos correlacionados. Como consequência, melhora o planejamento da produção e aprimora a análise de custos de produção desses produtos. A validação dessa redução de dados é dada com a análise de agrupamento também chamada de análise de cluster, que também é uma estatística multivariada

Suporte social, sentido de imortalidade simbólica e ansiedade perante a morte em familiares de utentes internados em Unidades de Cuidados Continuados

O presente estudo pretende ser um contributo para uma maior compreensão da Satisfação com o Suporte Social, do Sentido de Imortalidade Simbólica e da Ansiedade Perante a Morte em familiares de utentes internados em Unidades de Cuidados Continuados. Concretamente pretende-se verificar se estas três dimensões variam em função do tipo de Unidade de Cuidados Continuados em que o familiar se encontra internado e em função da situação de internamento (internado vs alta). No estudo I participaram 332 familiares de utentes internados em Unidades de Convalescença, em Unidades de Longa Duração e Manutenção e em Unidades de Cuidados Paliativos. Os participantes foram avaliados através da Escala de Satisfação com o Suporte Social de Pais Ribeiro (1999) e das versões portuguesas de Santos (1999) das Escalas de Sentido de Imortalidade Simbólica (Drolet, 1990) e de Ansiedade Perante a Morte (Templer, 1970). Em termos gerais, não foram observadas diferenças significativas entre os grupos para a maioria das variáveis consideradas, exceto no que diz respeito aos modos biossocial e natural que integram a Escala de Sentido de Imortalidade Simbólica. No que diz respeito ao modo biossocial, os familiares de utentes internados em Unidades de Convalescença foram os que apresentaram maior sentido de imortalidade simbólica comparativamente com os familiares de utentes em Unidades de Cuidados Paliativos. Já quanto ao modo natural, foi o grupo de familiares de utentes em Unidades de Cuidados Paliativos que evidenciou maior sentido de imortalidade simbólica em relação aos dois outros grupos. No estudo II participaram 259 familiares de utentes internados e com alta do internamento da Unidade de Convalescença. Estes foram igualmente observados através das três escalas utilizadas no estudo anterior, não tendo sido observadas diferenças siginificativas entre os dois grupos para nenhuma das variáveis ou factores considerados. Por fim, constatou-se, em ambos os estudos, a existência de uma correlação positiva entre a Satisfação com o Suporte Social e o Sentido de Imortalidade Simbólica e, ao contrário do descrito na literatura (que aponta para correlações negativas), entre estas duas variáveis e a Ansiedade Perante a Morte.This study aims to contribute to a deeper understanding of the Satisfaction with the Social Support, of the Symbolic Immortality and of the Anxiety Facing Death in the family members of patients hospitalized in Continuing Care Units. Specifically, it aims to verify if these three dimensions are affected by the typology of the Continuing Care Unit and by the state of the family member hospitalization (hospitalized versus hospital release). Three hundred and thirty two family members of patients hospitalized in Convalescence Units, in Long Term and Maintenance Units, and in Palliative Care Units participated in Study I. The participants were assessed with the Satisfaction with Social Support Scale (Pais Ribeiro, 1999) and with the Portuguese versions (Santos, , 1999) of the Symbolic Immortality Sense (Drolet, 1990) and Anxiety Facing Death (Templer, 1970) Scales. Overall, no significant differences were observed between groups for the majority of the considered variables, except for the bio-social and natural modes of Symbolic Immortality Sense Scale. For the bio-social mode, the group of family members of patients hospitalized in Convalescence Units showed a sense of symbolic immortality higher than the group of family members of patients in Palliative Care Units. For the natural way, it was the group of family members of patients in Palliative Care Units that showed higher sense of symbolic immortality compared to the other two groups. In study II, 259 family members of hospitalized patients and of hospital release patients from the Convalescence Unit were assessed. The same three scales used in the previous study were administered to the participants. No significant differences were observed between the two groups for any of the variables or factors considered. Finally, a positive correlation between the Satisfaction with the Social Support and the Sense of Symbolic Immortality was observed in both studies. A positive correlation between these two variables and Anxiety Facing Death were also observed.La présente étude à l’attention d’être une contribution pour une meilleure compréhension de la Satisfaction avec le Support Social, Immortalité Symbolique et l’Anxiété Envers la Mort en familier d’usagers hospitalisés dans des Unités de Soins Continus. Concrètement, cette étude doit permettre de vérifier si ces trois dimensions varient en fonction du type des Unités de Soins Continus où la personne de la famille est hospitalisée et en fonction de la situation de l’hospitalisation (hospitalisé vs sortie de l’hôpital). À l’étude I ont participé 332 familiers d’usagers hospitalisés aux Unités de Convalescence, aux Unités de Soins de Longue Durée et Soins d'Entretien, et dans les Unités des Soins Palliatifs. Les participants ont été évalués à travers de l’Échelle de Satisfaction avec le Support Social de Pais Ribeiro (1999) et des versions portugaises de Santos (1999) et Échelles du Sens de l'Immortalité Symbolique (Drolet, 1990) et de l’Anxiété Envers la Mort (Templer, 1970). En termes généraux, aucune différence significative n’a été observée entre les groupes pour la plupart des variables considérées, sauf par rapport aux modes bio-sociales et naturels qui intègrent l’Échelle du Sens de l'Immortalité Symbolique. Par rapport au mode bio-social, les familiers des usagers hospitalisés aux Unités de Convalescence ont été ceux qui ont présenté une plus grande sens de l'immortalité symbolique comparativement aux familiers des usagers hospitalisés dans les Unités des Soins Palliatifs. Quant à la manière naturelle, le groupe des familiers hospitalisés dans les Unités des Soins Palliatifs a été celui qui a mis en évidence des niveaux de l'immortalité symbolique plus élevés. À l’étude II ont participé 259 familiers d’usagers hospitalisés et avec sortie de l’hôpital d'Unité de Convalescence. Ceux-ci ont été également observés à travers des trois échelles utilisées dans l’étude antérieure, n’ayant été observés aucune différence significative entre les deux groupes pour aucune des variables ou facteurs considérés. À la fin, il a été constaté, dans les deux études, l'existence de la corrélation positive entre la Satisfaction avec le Support Social et le Sens de l'Immortalité Symbolique et, contrairement à ce qui est décrit dans la littérature (qui oriente vers des corrélations négatives), entre ces deux variables et l’Anxiété Envers la Mort

A Manutenção de Mata Ciliar: Um Ativo Permanente

A manutenção de mata ciliar permite a condição de conectividade entre áreas específicas, além de manter a diversidade biológica e assegurar a conservação in situ da variedade genética existente. O controle da erosão laminar (e/ou eólica) e genética, a preservação da qualidade das águas, a manutenção da biota do rio ou riacho, o controle de pragas e/ou de ervas invasoras, são algumas das vantagens existentes nessa manutenção, cuja classificação na Contabilidade Rural, compreende-se, deve ser no Ativo Permanente. Essa é a principal idéia deste Artigo

Intraretinal Fluid Detection by Means of a Densely Connected Convolutional Neural Network Using Optical Coherence Tomography Images

[Abstract] Hereby we present a methodology with the objective of detecting retinal fluid accumulations in between the retinal layers. The methodology uses a robust Densely Connected Neural Network to classify thousands of subsamples, extracted from a given Optical Coherence Tomography image. Posteriorly, using the detected regions, it satisfactorily generates a coherent and intuitive confidence map by means of a voting strategy.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-047Xunta de Galicia;ED481A-2019/196This research was funded by Instituto de Salud Carlos III grant number DTS18/00136, Ministerio de Economía y Competitividad grant number DPI 2015-69948-R, Xunta de Galicia through the accreditation of Centro Singular de Investigación 2016–2019, Ref. ED431G/01, Xunta de Galicia through Grupos de Referencia Competitiva, Ref. ED431C 2016-047 and Xunta de Galicia predoctoral grant contract ref. ED481A-2019/19

Automatic Identification of Diabetic Macular Edema Using a Transfer Learning-Based Approach

[Abstract] This paper presents a complete system for the automatic identification of pathological Diabetic Macular Edema (DME) cases using Optical Coherence Tomography (OCT) images as source of information. To do so, the system extracts a set of deep features using a transfer learning-based approach from different fully-connected layers and different pre-trained Convolutional Neural Network (CNN) models. Next, the most relevant subset of deep features is identified using representative feature selection methods. Finally, a machine learning strategy is applied to train and test the potential of the identified deep features in the pathological classification process. Satisfactory results were obtained, demonstrating the suitability of the presented system to filter those pathological DME cases, helping the specialist to optimize their diagnostic procedures.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-047This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds through the DTS18/00136 research project and by Ministerio de Ciencia, Innovación y Universidades, Government of Spain through the DPI2015-69948-R and RTI2018-095894-B-I00 research projects. Also, this work has received financial support from the European Union (European Regional Development Fund—ERDF) and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016–2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047

COVID-19 Lung Radiography Segmentation by Means of Multiphase Transfer Learning

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.[Abstract] COVID-19 is characterized by its impact on the respiratory system and, during the global outbreak of 2020, specific protocols had to be designed to contain its spread within hospitals. This required the use of portable X-ray devices that allow for a greater flexibility in terms of their arrangement in rooms not specifically designed for such purpose. However, their poor image quality, together with the subjectivity of the expert, can hinder the diagnosis process. Therefore, the use of automatic methodologies is advised. Even so, their development is challenging due to the scarcity of available samples. For this reason, we present a COVID-19-specific methodology able to segment these portable chest radiographs with a reduced number of samples via multiple transfer learning phases. This allows us to extract knowledge from two related fields and obtain a robust methodology with limited data from the target domain. Our proposal aims to help both experts and other computer-aided diagnosis systems to focus their attention on the region of interest, ignoring unrelated information.Instituto de Salud Carlos III, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project, Ayudas para la formación de profesorado universitario (FPU), grant ref. FPU18/02271; Ministerio de Ciencia e Innovación, Government of Spain through the research project with reference PID2019-108435RB-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, Grupos de Referencia Competitiva, grant ref. ED431C 2020/24 and through the postdoctoral grant contract ref. ED481B 2021/059; Axencia Galega de Innovación (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%)Xunta de Galicia; ED431C 2020/24Xunta de Galicia; ED481B 2021/059Xunta de Galicia; IN845D 2020/38Xunta de Galicia; ED431G 2019/0

Multivendor fully automatic uncertainty management approaches for the intuitive representation of DME fluid accumulations in OCT images

[Abstract]: Diabetes represents one of the main causes of blindness in developed countries, caused by fluid accumulations in the retinal layers. The clinical literature defines the different types of diabetic macular edema (DME) as cystoid macular edema (CME), diffuse retinal thickening (DRT), and serous retinal detachment (SRD), each with its own clinical relevance. These fluid accumulations do not present defined borders that facilitate segmentational approaches (specially the DRT type, usually not taken into account by the state of the art for this reason) so a diffuse paradigm is used for its detection and visualization. In this paper, we propose three novel approaches for the representation and characterization of these types of DME. A baseline proposal, using a convolutional neural network as backbone, another based on transfer learning from a general domain, and a third approach exploiting information of regions without a defined label. Overall, our baseline proposal obtained an AUC of 0.9583 ± 0.0093, the approach pretrained with a general-domain dataset an AUC of 0.9603 ± 0.0087, and the approach pretrained in the domain taking advantage of uncertainty, an AUC of 0.9619 ± 0.0073.Ministerio de Ciencia e Innovación; RTI2018-095894-B-I00Instituto de Salud Carlos III; DTS18/00136Ministerio de Ciencia e Innovación; FPU18/02271Ministerio de Ciencia e Innovación; PID2019-108435RB-I00Xunta de Galicia; ED431C 2020/24Xunta de Galicia; ED481B 2021/059Axencia Galega de Innovación; IN845D 2020/38Xunta de Galicia; ED431G 2019/0