Abstract

Abstract

Abstract

Abstract

Abstract
The use of 360 degrees videos has been increasing steadily in the 2010s, as content creators and users search for more immersive experiences. The freedom to choose where to look at during the video may hinder the overall experience instead of enhancing it, as there is no guarantee that the user will focus on relevant sections of the scene. Visual annotations superimposed on the video, such as text boxes or arrow icons, can help guide the user through the narrative of the video while maintaining freedom of movement. This paper presents a web-based immersive visualizer for 360. videos that contain dynamic media annotations, rendered in real-time. A set of annotations was created with the purpose of providing information or guiding the user to points of interest. The visualizer can be used with a computer, using a keyboard and mouse or HTC Vive, and in mobile devices with Cardboard VR headsets, to experience the video in virtual reality, which is made possible with the WebVR API. The visualizer was evaluated through usability tests, to analyze the impact of different annotation techniques on the users' experience. The obtained results demonstrate that annotations can assist in guiding the user during the video, and a careful design is imperative so that they are not intrusive and distracting for the viewers.

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.