Abstract
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images. iW-Net is composed of two blocks: the first one provides an automatic segmentation and the second one allows to correct it by analyzing 2 points introduced by the user in the nodule's boundary. For this purpose, a physics inspired weight map that takes the user input into account is proposed, which is used both as a feature map and in the system's loss function. Our approach is extensively evaluated on the public LIDC-IDRI dataset, where we achieve a state-of-the-art performance of 0.55 intersection over union vs the 0.59 inter-observer agreement. Also, we show that iW-Net allows to correct the segmentation of small nodules, essential for proper patient referral decision, as well as improve the segmentation of the challenging non-solid nodules and thus may be an important tool for increasing the early diagnosis of lung cancer.

Abstract
Diabetic Retinopathy is the leading cause of blindness in the working-age population of the world. The main aim of this paper is to improve the accuracy of Diabetic Retinopathy detection by implementing a shadow removal and color correction step as a preprocessing stage from eye fundus images. For this, we rely on recent findings indicating that application of image dehazing on the inverted intensity domain amounts to illumination compensation. Inspired by this work, we propose a Shadow Removal Layer that allows us to learn the preprocessing function for a particular task. We show that learning the pre-processing function improves the performance of the network on the Diabetic Retinopathy detection task.

Abstract
It is known that excessive levels of occupational stress affect professionals' technical and non-technical skills and surgeons are no exception. However, very few studies address this problem in neurosurgeons. A system for monitoring cardiovascular strain and autonomic imbalance during intracranial aneurysm procedures is proposed in order to obtain overall cardiac measures from those procedures. Additionally, this study also allows to detect stressful events and compare their impact with the surgeon's own appraisal. Linear and nonlinear heart rate variability (HRV) features were extracted from surgeon's electrocardiogram (ECG) signal using wearable ECG monitors and mobile technology during 10 intracranial aneurysm surgeries with two surgeons. Stress appraisal and cognitive workload were assessed using self-report measures. Findings suggest that the surgeon associated to the main role during the clipping can be exposed to high levels of stress, especially if a rupture occurs (pNN20 = 0%), while the assistant surgeon tends to experience mental fatigue. Cognitive workload scores of one of the surgeons were negatively correlated with AVNN, SDNN, pNN20, pNN50, 1 V, 2 L V, SD2 and CVI measures. Cognitive workload was positively related with stress appraisal, suggesting that more mentally demanding procedures are also assessed as more stressful. Finally, pNN20 seems to better mirror behavior during stress moments than pNN50. Additionally, a sympathovagal excitation occurs in one of the professionals after changing to main role. The present methodology shows potential for the identification of harmful events. This work may be of importance for the design of effective interventions in order to reduce surgeons stress levels. Furthermore, this approach can be applied to other professions.

Abstract
Objectives: This study sought to evaluate whether velocity of naturally occurring myocardial shear waves (SW) could relate to myocardial stiffness (MS) in vivo. Background: Cardiac SW imaging has been proposed as a noninvasive tool to assess MS. SWs occur after mechanical excitation of the myocardium (e.g., mitral valve closure [MVC] and aortic valve closure [AVC]), and their propagation velocity is theoretically related to MS, thus providing an opportunity to assess stiffness at end-diastole (ED) and end-systole. However, given that SW propagation in vivo is complex, it remains unclear whether natural SW velocity effectively relates to MS. Methods: This study prospectively enrolled 50 healthy volunteers (HV) (43.7 ± 17.1 years of age) and 18 patients with cardiac amyloidosis (CA) (68.0 ± 9.8 years of age). HV were divided into 3 age groups: group I, 20 to 39 years of age (n = 24); group II, 40 to 59 years of age (n = 11); and group III, 60 to 80 years of age (n = 15). Parasternal long-axis views were acquired using an experimental scanner. Tissue (Doppler) acceleration maps were extracted from an anatomical M-mode along the midline of the left ventricular septum. Results: SW propagation velocity was significantly higher in CA patients than in HV after both MVC (3.54 ± 0.93 m/s vs. 6.33 ± 1.63 m/s, respectively; p < 0.001) and AVC (3.75 ± 0.76 m/s vs. 5.63 ± 1.13 m/s, respectively; p < 0.001). Similarly, SW propagation velocity differed significantly among age groups in HV, with a significantly higher value for group III than for group I, both occurring after MVC (p < 0.001) and AVC (p < 0.01). Moreover, SW propagation velocity after MVC was found to be significantly higher in patients with an increasing grade of diastolic dysfunction (p < 0.001). Finally, positive correlation was found between SW velocities after MVC and mitral inflow-to-mitral relaxation velocity ratio (E/E') (r = 0.74; p = 0.002). Conclusions: End-diastole SW velocities were significantly higher in patients with CA, patients with a higher grade of diastolic dysfunction, and elderly volunteers. These findings thus suggest that the speed of naturally induced SWs may be related to MS. © 2019 American College of Cardiology Foundation

Abstract
Colloids and suspensions are part of our daily routines. Even the blood is considered a "naturally" occurring colloid. However, the majority of colloids are complex and composed by a diversity of nano to microparticles. The characterization of both synthetic and physiological fluids in terms of particulate types, size and surface characteristics plays a vital role in products formulation, and in the early diagnosis through the identification of abnormal scatterers in physiological fluids, respectively. Several methods have been proposed for characterizing suspensions, including imaging, electrical sensing counters, hydrodynamic or field flow fractionation. However, the Dynamic Light Scattering (DLS) has evolved as the most convenient method from these. Based also on the scattering signal, we propose a novel, simple and fast method able to determine the number of different scatterers type present in a suspension, without any previous information about its composition (in terms of particle classes). This is achieved by collecting features from a 980 nm laser back-scattered signal acquired through a polymeric lensed optical fiber tip dipped into the solution. Unlike DLS, this technique allows the trapping of particles whose diameter >= 1 mu m. For smaller particles, despite not guaranteeing their immobilization, it is also able to determine the number of different nanoparticles classes in an ensemble. The number of particle types was correctly determined for suspensions of synthetic particles and yeasts; different bacteria; and 100 nm nanoparticles types, using both Principal Component Analysis and K-means algorithms. This method could be a valuable alternative to complex and time-consuming methods for particles separation, such as field flow fractionation.

Abstract
Under the VitalResponder® (VR) line of research, mostly funded by the Carnegie Mellon University (CMU)-Portugal program, we have been developing, in partnership with colleagues from CMU, novel wearable monitoring solutions for hazardous professionals such as first responders (FR). We are exploring the synergy between innovative wearable technologies, scattered sensor network and precise localization to provide secure, reliable and effective first-response information services in emergency scenarios. This enables a thorough teams’management, namely on FR exposure to different hazardous elements, effort levels and critical situations that contribute to team members’ stress and fatigue levels. © The Institution of Engineering and Technology 2017.