Abstract
Cardiovascular disease is the leading cause of death around the world and its early detection is a key to improving long-term health outcomes. To detect possible heart anomalies at an early stage, an automatic method enabling cardiac health low-cost screening for the general population would be highly valuable. By analyzing the phonocardiogram (PCG) signals, it is possible to perform cardiac diagnosis and find possible anomalies at an early-term. Accordingly, the development of intelligent and automated analysis tools of the PCG is very relevant. In this work, the PCG signals are studied with the main objective of determining whether a PCG signal corresponds to a “normal” or “abnormal” physiological state. The main contribution of this work is the evidence provided that time domain features can be combined with features extracted from a wavelet transformation of PCG signals to improve automatic cardiac disease classification. We empirically demonstrate that, from a pool of alternatives, the best classification results are achieved when both time and wavelet features are used by a Support Vector Machine with a linear kernel. Our approach has obtained better results than the ones reported by the challenge participants which use large amounts of data and high computational power. © Springer Nature Switzerland AG 2019.

Abstract
Central serous chorioretinopathy is a retinal disease in which there is a leak of fluid into the subretinal space resulting in mild to moderate loss of visual acuity. Sequences of images from a fluorescein angiography exam are most of the times used for analyzing these leaks. This work presents a diagnostic aid method to detect and characterize the progression of fluid area along the exam, in order to provide a second opinion and increase the focus and the speed of analysis of the ophthalmologists. The method is based on a comparative approach by image subtraction between the late and early frames. The obtained segmentation results are quite promising with an average Dice coefficient of 0.801 +/- 0.106 for the training set and 0.774 +/- 0.106 for the test set.

Abstract
This study describes a novel dataset with retinal image quality annotation, defined by three different retinal experts, and presents an inter-observer analysis for quality assessment that can be used as gold-standard for future studies. A state-of-the-art algorithm for retinal image quality assessment is also analysed and compared against the specialists performance. Results show that, for 71% of the images present in the dataset, the three experts agree on the given image quality label. The results obtained for accuracy, specificity and sensitivity when comparing one expert against another were in the ranges [83.0 - 85.2]%, [72.7 - 92.9]% and [80.0 - 94.7]%, respectively. The evaluated automatic quality assessment method, despite not being trained on the novel dataset, presents a performance which is within inter-observer variability.

Abstract
Lung volume segmentation is a key step in the design of Computer-Aided Diagnosis systems for automated lung pathology analysis. However, isolating the lung from CT volumes can he a challenging process due to considerable deformations and the potential presence of pathologies. Convolutional Neural Networks (CNN) are effective tools for modeling the spatial relationship between lung voxels. Unfortunately, they typically require large quantities of annotated data, and manually delineating the lung from volumetric CT scans can he a cumbersome process. We propose to train a 3D CNN to solve this task based on semi-automatically generated annotations. For this, we introduce an extension of the well-known V-Net architecture that can handle higher dimensional input data. Even if the training set labels are noisy and contain errors, our experiments show that it is possible to learn to accurately segment the lung relying on them. Numerical comparisons on an external test set containing lung segmentations provided by a medical expert demonstrate that the proposed model generalizes well to new data, reaching an average 98.7% Dice coefficient. The proposed approach results in a superior performance with respect to the standard V-Net model, particularly on the lung boundary.

Abstract
When dealing with electrocardiography (ECG) the main focus relies on the classification of the heart's electric activity and deep learning has been proving its value over the years classifying the heartbeats, exhibiting great performance when doing so. Following these assumptions, we propose a deep learning model based on a ResNet architecture with convolutional ID layers to classes the beats into one of the 4 classes: normal, atrial premature contraction, premature ventricular contraction and others. Experimental results with MIT-BIH Arrhythmia Database confirmed that the model is able to perform well, obtaining an accuracy of 96% when using stochastic gradient descent (SGD) and 83% when using adaptive moment estimation (Adam), SGD also obtained F1-scores over 90% for the four classes proposed. A larger dataset was created and tested as unforeseen data for the trained model, proving that new tests should be done to improve the accuracy of it.

Abstract
Regional contribution to left ventricular (LV) ejection is of much clinical importance but its assessment is notably challenging. While deformation imaging is often used, this does not take into account loading conditions. Recently, a method for intraventricular pressure estimation was proposed, thus allowing for loading conditions to be taken into account in a non-invasive way. In this work, a method for 3D automatic myocardial performance mapping in echocardiography is proposed by performing 3D myocardial segmentation and tracking, thus giving access to local geometry and strain. This is then used to assess local LV stress-strain relationships which can be seen as a measure of local myocardial work. The proposed method was validated against F-18-fluorodeoxyglucose positron emission tomography, the reference method to clinically assess local metabolism. Averaged over all patients, the mean correlation between FDG-PET and the proposed method was 0.67 +/- 0.18. In conclusion, stress-strain loops were, for the first time, estimated from 3D echocardiography and correlated to the clinical gold standard for local metabolism, showing the future potential of real-time 3D echocardiography ( RT3DE) for the assessment of local metabolic activity of the heart.