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.

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.

Nowadays, among the main causes of blindness in developed countries are age-related macular degeneration (AMD) and the diabetic macular edema (DME). Both diseases present, as a common symptom, the appearance of cystoid fluid regions inside the retinal layers. Optical coherence tomography (OCT) image modality was one of the main medical imaging techniques for the early diagnosis and monitoring of AMD and DME via this intraretinal fluid detection and characterization. We present a novel methodology to identify these fluid accumulations by means of generating binary maps (offering a direct representation of these areas) and heat maps (containing the region confidence). To achieve this, a set of 312 intensity and texture-based features were studied. The most relevant features were selected using the sequential forward selection (SFS) strategy and tested with three archetypal classifiers: LDC, SVM and Parzen window. Finally, the most proficient classifier is used to create the proposed maps. All of the tested classifiers returned satisfactory results, the best classifier achieving a mean test accuracy higher than 94% in all of the experiments. The suitability of the maps was evaluated in a context of a screening issue with three different datasets obtained with two different devices, testing the capabilities of the system to work independently of the used OCT device. The experiments with the map creation were performed using 323 OCT images. Using only the binary maps, more than 91.33% of the images were correctly classified. With only the heat maps, the proposed methodology correctly separated 93.50% of the images.

The recent human coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared as a global pandemic on 11 March 2020 by the World Health Organization. Given the effects of COVID-19 in pulmonary tissues, chest radiography imaging plays an important role for 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, two complementary deep learning approaches based on a densely convolutional network architecture are herein presented. The joint response of both 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 provided by the Radiology Service of the Complexo Hospitalario Universitario A Coru&ntildea (CHUAC) specifically retrieved for this research. Despite the poor quality of chest X-Ray images that is inherent to the nature of the portable equipment, the proposed approaches provided satisfactory results, allowing a reliable analysis of portable radiographs, to support the clinical decision-making process.

No abstract

Diabetic Retinopathy and Diabetic Macular Edema (DME) represent one of the main causes of blindness in developed countries. They are characterized by fluid deposits in the retinal layers, causing a progressive vision loss over the time. The clinical literature defines three DME types according to the texture and disposition of the fluid accumulations: Cystoid Macular Edema (CME), Diffuse Retinal Thickening (DRT) and Serous Retinal Detachment (SRD). Detecting each one is essential as, depending on their presence, the expert will decide on the adequate treatment of the pathology. In this work, we propose a robust detection and visualization methodology based on the analysis of independent image regions. We study a complete and heterogeneous library of 375 texture and intensity features in a dataset of 356 labeled images from two of the most used capture devices in the clinical domain: a CIRRUSTM HD-OCT 500 Carl Zeiss Meditec and 179 OCT images from a modular HRA + OCT SPECTRALIS® from Heidelberg Engineering, Inc. We extracted 33,810 samples for each type of DME for the feature analysis and incremental training of four different classifier paradigms. This way, we achieved an 84.04% average accuracy for CME, 78.44% average accuracy for DRT and 95.40% average accuracy for SRD. These models are used to generate an intuitive visualization of the fluid regions. We use an image sampling and voting strategy, resulting in a system capable of detecting and characterizing the three types of DME presenting them in an intuitive and repeatable way.

Currently, optical coherence tomography is one of the most used medical imaging modalities, offering cross-sectional representations of the studied tissues. This image modality is specially relevant for the analysis of the retina, since it is the internal part of the human body that allows an almost direct examination without invasive techniques. One of the most representative cases of use of this medical imaging modality is for the identification and characterization of intraretinal fluid accumulations, critical for the diagnosis of one of the main causes of blindness in developed countries: the Diabetic Macular Edema. The study of these fluid accumulations is particularly interesting, both from the point of view of pattern recognition and from the different branches of health sciences. As these fluid accumulations are intermingled with retinal tissues, they present numerous variants according to their severity, and change their appearance depending on the configuration of the device; they are a perfect subject for an in-depth research, as they are considered to be a problem without a strict solution. In this work, we propose a comprehensive and detailed analysis of the patterns that characterize them. We employed a pool of 11 different texture and intensity feature families (giving a total of 510 markers) which we have analyzed using three different feature selection strategies and seven complementary classification algorithms. By doing so, we have been able to narrow down and explain the factors affecting this kind of accumulations and tissue lesions by means of machine learning techniques with a pipeline specially designed for this purpose.