Abstract
Retinal vessel segmentation is a fundamental and well-studied problem in the retinal image analysis field. The standard images in this context are color photographs acquired with standard fundus cameras. Several vessel segmentation techniques have been proposed in the literature that perform successfully on this class of images. However, for other retinal imaging modalities, blood vessel extraction has not been thoroughly explored. In this paper, we propose a vessel segmentation technique for Scanning Laser Opthalmoscopy (SLO) retinal images. Our method adapts a Deep Neural Network (DNN) architecture initially devised for segmentation of biological images (U-Net), to perform the task of vessel segmentation. The model was trained on a recent public dataset of SLO images. Results show that our approach efficiently segments the vessel network, achieving a performance that outperforms the current state-of-the-art on this particular class of images. © Springer International Publishing AG 2017.

Abstract

Abstract
The Arteriolar-to-Venular Ratio (AVR) is a popular dimensionless measure which allows the assessment of patients' condition for the early diagnosis of different diseases, including hypertension and diabetic retinopathy. This paper presents two new approaches for AVR computation in retinal photographs which include a sequence of automated processing steps: vessel segmentation, caliber measurement, optic disc segmentation, artery/vein classification, region of interest delineation, and AVR calculation. Both approaches have been tested on the INSPIRE-AVR dataset, and compared with a ground-truth provided by two medical specialists. The obtained results demonstrate the reliability of the fully automatic approach which provides AVR ratios very similar to at least one of the observers. Furthermore, the semi-automatic approach, which includes the manual modification of the artery/vein classification if needed, allows to significantly reduce the error to a level below the human error.

Abstract

Abstract
Current mobile devices allow the execution of sophisticated applications with the capacity for identifying the user situation, which can be helpful in treatments of mental disorders. In this paper, we present SituMan, a solution that provides situation awareness to MoodBuster, an ecological momentary assessment and intervention mobile application used to request self-assessments from patients in depression treatments. SituMan has a fuzzy inference engine to identify patient situations using context data gathered from the sensors embedded in mobile devices. Situations are specified jointly by the patient and mental health professional, and they can represent the patient's daily routine (e.g., "studying", "at work", "working out"). MoodBuster requests mental status self-assessments from patients at adequate moments using situation awareness. In addition, SituMan saves and displays patient situations in a summary, delivering them for consultation by mental health professionals. A first experimental evaluation was performed to assess the user satisfaction with the approaches to define and identify situations. This experiment showed that SituMan was well evaluated in both criteria. A second experiment was performed to assess the accuracy of the fuzzy engine to infer situations. Results from the second experiment showed that the fuzzy inference engine has a good accuracy to identify situations.

Abstract
Purpose: Police in Europe are facing increased demands and diminished resources, and this is particularly prominent among emergency response officers (EROs) working in poorer countries such as Portugal. Considering that daily stress and limited coping skills can result in detrimental consequences for officers’ health and society welfare, the purpose of this paper is to investigate stress and coping among Portuguese EROs. Design/methodology/approach: EROs completed daily diaries over 11 working days. Each diary entry included an open-ended stressor, coping section and a Likert-type scale to evaluate coping effectiveness. Data were analyzed using inductive and deductive content analysis procedures. The frequency of stressors, coping and coping effectiveness were calculated. Findings: EROs reported facing more operational stressors, particularly public disorder situations. However, gun situations were perceived as the most intense stressor. Emotion-focused coping (i.e. peer support) was more used than problem-focused. Despite variation in coping effectiveness in accordance to stressor experienced, longitudinal analysis suggests that problem-focused coping is more effective. Research limitations/implications: Longitudinal methodologies should contemplate stress appraisal and coping effectiveness in order to fully understand stress and coping. Future studies should employ this methodology at a larger scale and over longer periods. Practical implications: Intervention programs for EROs should be multidimensional, targeting work conditions and resources, stress management, and coping effectiveness. Originality/value: Findings provide strong recommendations for future research and applied implications for stress prevention and effective coping interventions. © 2017, © Emerald Publishing Limited.