The recent human coronavirus disease (COVID-19) is a respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the effects of COVID-19 in pulmonary tissues, chest radiography imaging plays an important role in the screening, early detection, and monitoring of the suspected individuals. Hence, as the pandemic of COVID-19 progresses, there will be a greater reliance on the use of portable equipment for the acquisition of chest X-ray images due to its accessibility, widespread availability, and benefits regarding to infection control issues, minimizing the risk of cross-contamination. This work presents novel fully automatic approaches specifically tailored for the classification of chest X-ray images acquired by portable equipment into 3 different clinical categories: normal, pathological, and COVID-19. For this purpose, 3 complementary deep learning approaches based on a densely convolutional network architecture are herein presented. The joint response of all the approaches allows to enhance the differentiation between patients infected with COVID-19, patients with other diseases that manifest characteristics similar to COVID-19 and normal cases. The proposed approaches were validated over a dataset specifically retrieved for this research. Despite the poor quality of the chest Xray images that is inherent to the nature of the portable equipment, the proposed approaches provided global accuracy values of 79.62%, 90.27% and 79.86%, respectively, allowing a reliable analysis of portable radiographs to facilitate the clinical decision-making process.

One of the main challenges in times of sanitary emergency is to quickly develop computer aided diagnosis systems with a limited number of available samples due to the novelty, complexity of the case and the urgency of its implementation. This is the case during the current pandemic of COVID-19. This pathogen primarily infects the respiratory system of the afflicted, resulting in pneumonia and in a severe case of acute respiratory distress syndrome. This results in the formation of different pathological structures in the lungs that can be detected by the use of chest X-rays. Due to the overload of the health services, portable X-ray devices are recommended during the pandemic, preventing the spread of the disease. However, these devices entail different complications (such as capture quality) that, together with the subjectivity of the clinician, make the diagnostic process more difficult and suggest the necessity for computer-aided diagnosis methodologies despite the scarcity of samples available to do so. To solve this problem, we propose a methodology that allows to adapt the knowledge from a well-known domain with a high number of samples to a new domain with a significantly reduced number and greater complexity. We took advantage of a pre-trained segmentation model from brain magnetic resonance imaging of a unrelated pathology and performed two stages of knowledge transfer to obtain a robust system able to segment lung regions from portable X-ray devices despite the scarcity of samples and lesser quality. This way, our methodology obtained a satisfactory accuracy of for patients with COVID-19, for normal patients and for patients with pulmonary diseases with similar characteristics as COVID-19 (such as pneumonia) but not genuine COVID-19.

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 complementary 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 face these classification tasks, we exploited and adapted to this topic a densely convolutional network architecture, which connects each layer to every other layer in a feed-forward fashion. To validate the designed approaches, several representative experiments were performed using images retrieved from different public chest X-ray images datasets. Overall, satisfactory results were obtained from the designed experiments, facilitating the doctors' work and allowing better an early diagnostic/screening and treatment of this relevant pandemic pathology.

A Tomografia de Coerência Óptica (TCO) consiste numa técnica de imagem médica não invasiva e ausente do contacto directo com o paciente que avalia in vivo a histopatologia do tecido. Esta tem sido amplamente utilizada no diagnóstico médico para identificar diversas patologias, não só associadas ao órgão em estudo no presente projecto, mas também a outras doenças em geral.A Retinopatia Diabética (RD) e a Degeneração Macular Relacionada à Idade (DMRI) são atualmente consideradas como duas das principais doenças oculares relacionadas com a perda de visão nos países desenvolvidos. Estas doenças têm em comum o acúmulo de líquido dentro do tecido retiniano, formando o Edema Macular (EM). A detecção e caracterização da acumulação de líquido intra-retiniano na mácula constitui uma questão oftalmológica crucial, dado que este fornece informações úteis na identificação e no diagnóstico dos diferentes tipos de EM, indicando consequentemente a gravidade da doença. Estes tipos de edemas da retina são clinicamente definidos, segundo a classificação de referência do campo, como: Descolamento Seroso Exsudativo da Retina (DSER), Edema Macular Difuso (EMD) e Edema Macular Cistoide (EMC).O presente trabalho visa o desenvolvimento de um sistema automático capaz de detectar e caracterizar os três tipos de EM usando imagens TCO. Este sistema de Diagnóstico Assistido por Computador (DAC) usará diferentes estratégias as quais combinam técnicas de processamento de imagem e de aprendizagem máquina com o conhecimento clínico. No caso do reconhecimento dos edemas DSER e EMC, espera-se a aplicação de abordagens com base nos diferentes limiares de intensidades característicos destes edemas, enquanto que para os EMD, dada a sua complexidade e aparência regional, deve ser aplicada uma estratégia de aprendizagem para explorar propriedades características dessa região.Esta solução permitirá assegurar a redução de custos relacionados com operações cirúrgicas e tratamento clínico aumentando assim a qualidade do diagnóstico, reduzindo a subjectividade inter-especialistas culminando na melhora da qualidade de vida do paciente. A produtividade e eficiência dos especialistas irá igualmente aumentar dado que esta solução irá o seu trabalho será facilitado. AbstractThe Optical Coherence Tomography (OCT) consists of a non-invasive and contactless medical image technique that evaluates in vivo the histopathology of the tissue. It has been widely used in medical diagnosis to identify a large range of pathologies, not only associated to the organ under the present study but also with other diseases in general.The diabetic retinopathy (DR) and the Age-related Macular Degeneration (AMD) are considered the two major eye diseases that are currently related with the vision loss in the developed countries. These diseases have in common the fluid accumulation within the retinal tissue round the macular region, forming the Macular Edema (ME). The detection and characterization of the intraretinal fluid accumulation constitutes a crucial ophthalmological issue as i...

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

The current COVID-19 pandemic, that has caused more than 100 million cases as well as more than two million deaths worldwide, demands the development of fast and accurate diagnostic methods despite the lack of available samples. This disease mainly affects the respiratory system of the patients and can lead to pneumonia and to severe cases of acute respiratory syndrome that result in the formation of several pathological structures in the lungs. These pathological structures can be explored taking advantage of chest X-ray imaging. As a recommendation for the health services, portable chest X-ray devices should be used instead of conventional fixed machinery, in order to prevent the spread of the pathogen. However, portable devices present several problems (specially those related with capture quality). Moreover, the subjectivity and the fatigue of the clinicians lead to a very difficult diagnostic process. To overcome that, computer-aided methodologies can be very useful even taking into account the lack of available samples that the COVID-19 affectation shows. In this work, we propose an improvement in the performance of COVID-19 screening, taking advantage of several cycle generative adversarial networks to generate useful and relevant synthetic images to solve the lack of COVID-19 samples, in the context of poor quality and low detail datasets obtained from portable devices. For validating this proposal for improved COVID-19 screening, several experiments were conducted. The results demonstrate that this data augmentation strategy improves the performance of a previous COVID-19 screening proposal, achieving an accuracy of 98.61% when distinguishing among NON-COVID-19 ( i.e. normal control samples and samples with pathologies others than COVID-19) and genuine COVID-19 samples. It is remarkable that this methodology can be extrapolated to other pulmonary pathologies and even other medical imaging domains to overcome the data scarcity.

No abstract

Optical Coherence Tomography (OCT) has become a relevant image modality in the ophthalmological clinical practice, as it offers a detailed representation of the eye fundus. This medical imaging modality is currently one of the main means of identification and characterization of intraretinal cystoid regions, a crucial task in the diagnosis of exudative macular disease or macular edema, among the main causes of blindness in developed countries. This work presents an exhaustive analysis of intensity and texture-based descriptors for its identification and classification, using a complete set of 510 texture features, three state-of-the-art feature selection strategies, and seven representative classifier strategies. The methodology validation and the analysis were performed using an image dataset of 83 OCT scans. From these images, 1609 samples were extracted from both cystoid and non-cystoid regions. The different tested configurations provided satisfactory results, reaching a mean cross-validation test accuracy of 92.69%. The most promising feature categories identified for the issue were the Gabor filters, the Histogram of Oriented Gradients (HOG), the Gray-Level Run-Length matrix (GLRL), and the Laws’ texture filters (LAWS), being consistently and considerably selected along all feature selector algorithms in the top positions of different relevance rankings.