Abstract

Abstract
The integration of functional imaging modality provided by Positron Emission Tomography (PET) and associated anatomical imaging modality provided by Computed Tomography (CT) has become an essential procedure both in the evaluation of different types of malignancy and in radiotherapy planning. The alignment of these two exams is thus of great importance. In this research work, three registration approaches (1) intensity-based registration, (2) rigid translation followed by intensity-based registration and (3) coarse registration followed by fine-tuning were evaluated and compared. To characterize the performance of these methods, 161 real volume scans from patients involved in Hodgkin Lymphoma staging were used: CT volumes used for radiotherapy planning were registered with PET volumes before any treatment. Registration results achieved 78%, 60%, and 91% of accuracy for methods (1), (2) and (3), respectively. Registration methods validation was extended to a corresponding landmarks points distance calculation. Methods (1), (2) and (3) achieved a median improvement registration rate of 66% mm, 51% mm and 70% mm, respectively. The accuracy of the proposed methods was further confirmed by extending our experiments to other multimodal datasets and in a monomodal dataset with different acquisition conditions. © 2018, Springer Nature Switzerland AG.

Abstract
In this paper, deep convolutional neural networks are used to segment heart sounds into their main components. The proposed method is based on the adoption of a novel deep convolutional neural network architecture, which is inspired by similar approaches used for image segmentation. A further post-processing step is applied to the output of the proposed neural network, which induces the output state sequence to be consistent with the natural sequence of states within a heart sound signal (S1, systole, S2, diastole). The proposed approach is tested on heart sound signals longer than 5 seconds from the publicly available PhysioNet dataset, and it is shown to outperform current state-of-the-art segmentation methods by achieving an average sensitivity of 93.4% and an average positive predictive value of 94.5% in detecting S1 and S2 sounds.

Abstract

Abstract

Abstract
Although still little-known, glamping has become a nature-based tourism option for people who want a higher level of comfort. The offer of this type of accommodation is growing, namely in Portugal, but there are still few studies that address the motivations and other relevant factors explaining its adoption or refusal by consumers. The present study applied a qualitative approach aimed at exploring consumers’ motivations or obstacles for choosing glamping, and their perceptions as tourists on the differences between glamping and camping. Data were collected through the conduction of focus groups held in 2017 and content analysis techniques for contextualized interpretations were used. The most important motivational driver to go glamping is the direct contact with nature. Glamour, comfort, privacy and a different experience are also important aspects that consumers appreciate. Inversely, the main obstacles are the cost, the limited offer, the lack of knowledge, and the non-authenticity, compared to camping, of the offer.