Abstract
Rumination is a psychological process that is featured by a repetitive pattern of intrusive thoughts that, by focusing the person into painful cognitive contents, elicits suffering. Usually people try to deal with these ruminative thoughts by trying to avoid thinking, which paradoxically makes the thoughts even more intense. Several researchers proposed that ruminative thoughts are central in anxiety and depressive disorders and that the treatment of rumination is also pivotal in the treatment of these disorders. This study is a first attempt to construct a game that trains players in shifting the attention from ruminative thoughts to problem-solving tasks, illustrating the use of the game. We expect that training such an ability will reduce the intensity of that thoughts. It is our expectation that the continuous use of games with these kind of features will reduce the intensity of ruminative thoughts, improving the wellbeing and reducing the psychological suffering of players.

Abstract
Surgical training for minimal invasive kidney interventions (MIKI) has huge importance within the urology field. Within this topic, simulate MIKI in a patient-specific virtual environment can be used for pre-operative planning using the real patient's anatomy, possibly resulting in a reduction of intra-operative medical complications. However, the validated VR simulators perform the training in a group of standard models and do not allow patient-specific training. For a patient-specific training, the standard simulator would need to be adapted using personalized models, which can be extracted from pre-operative images using segmentation strategies. To date, several methods have already been proposed to accurately segment the kidney in computed tomography (CT) images. However, most of these works focused on kidney segmentation only, neglecting the extraction of its internal compartments. In this work, we propose to adapt a coupled formulation of the B-Spline Explicit Active Surfaces (BEAS) framework to simultaneously segment the kidney and the renal collecting system (CS) from CT images. Moreover, from the difference of both kidney and CS segmentations, one is able to extract the renal parenchyma also. The segmentation process is guided by a new energy functional that combines both gradient and region-based energies. The method was evaluated in 10 kidneys from 5 CT datasets, with different image properties. Overall, the results demonstrate the accuracy of the proposed strategy, with a Dice overlap of 92.5%, 86.9% and 63.5%, and a point-to-surface error around 1.6 mm, 1.9 mm and 4 mm for the kidney, renal parenchyma and CS, respectively. © 2018 IEEE.

Abstract

Abstract
In this paper, a method for estimation of human pose is proposed, making use of ToF (Time of Flight) cameras. For this, a YOLO based object detection method was used, to develop a top-down method. In the first stage, a network was developed to detect people in the image. In the second stage, a network was developed to estimate the joints of each person, using the image result from the first stage. We show that a deep learning network trained from scratch with ToF images yields better results than taking a deep neural network pretrained on RGB data and retraining it with ToF data. We also show that a top-down detector, with a person detector and a joint detector works better than detecting the body joints over the entire image.

Abstract
The human hand is the body part most frequently injured in occupational accidents, accounting for one out of five emergency cases and often requiring surgery with subsequently long periods of rehabilitation. This paper proposes a Virtual Reality game to improve conventional physiotherapy in hand rehabilitation, focusing on resolving recurring limitations reported in most technological solutions to the problem, namely the limited diversity support of movements and exercises, complicated calibrations and exclusion of patients with open wounds or other disfigurements of the hand. The system was assessed by seven able-bodied participants using a semistructured interview targeting three evaluation categories: hardware usability, software usability and suggestions for improvement. A System Usability Score (SUS) of 84.3 and participants' disposition to play the game confirm the potential of both the conceptual and technological approaches taken for the improvement of hand rehabilitation therapy. © 2020 IEEE.

Abstract
BackgroundSeveral studies link the seamless fit of implant-supported prosthesis with the accuracy of the dental impression technique obtained during acquisition. In addition, factors such as implant angulation and coping shape contribute to implant misfit. PurposeThe aim of this study was to identify the most accurate impression technique and factors affecting the impression accuracy. Material and MethodsA systematic review of peer-reviewed literature was conducted analyzing articles published between 2009 and 2013. The following search terms were used: implant impression, impression accuracy, and implant misfit. A total of 417 articles were identified; 32 were selected for review. ResultsAll 32 selected studies refer to in vitro studies. Fourteen articles compare open and closed impression technique, 8 advocate the open technique, and 6 report similar results. Other 14 articles evaluate splinted and non-splinted techniques; all advocating the splinted technique. Polyether material usage was reported in nine; six studies tested vinyl polysiloxane and one study used irreversible hydrocolloid. Eight studies evaluated different copings designs. Intraoral optical devices were compared in four studies. ConclusionsThe most accurate results were achieved with two configurations: (1) the optical intraoral system with powder and (2) the open technique with splinted squared transfer copings, using polyether as impression material.