Video-based augmented reality combining CT-scan and instrument position data to microscope view in middle ear surgery.

The aim of the study was to develop and assess the performance of a video-based augmented reality system, combining preoperative computed tomography (CT) and real-time microscopic video, as the first crucial step to keyhole middle ear procedures through a tympanic membrane puncture. Six different artificial human temporal bones were included in this prospective study. Six stainless steel fiducial markers were glued on the periphery of the eardrum, and a high-resolution CT-scan of the temporal bone was obtained. Virtual endoscopy of the middle ear based on this CT-scan was conducted on Osirix software. Virtual endoscopy image was registered to the microscope-based video of the intact tympanic membrane based on fiducial markers and a homography transformation was applied during microscope movements. These movements were tracked using Speeded-Up Robust Features (SURF) method. Simultaneously, a micro-surgical instrument was identified and tracked using a Kalman filter. The 3D position of the instrument was extracted by solving a three-point perspective framework. For evaluation, the instrument was introduced through the tympanic membrane and ink droplets were injected on three middle ear structures. An average initial registration accuracy of 0.21 ± 0.10 mm (n = 3) was achieved with a slow propagation error during tracking (0.04 ± 0.07 mm). The estimated surgical instrument tip position error was 0.33 ± 0.22 mm. The target structures' localization accuracy was 0.52 ± 0.15 mm. The submillimetric accuracy of our system without tracker is compatible with ear surgery.

WiseNET: An indoor multi-camera multi-space dataset with contextual information and annotations for people detection and tracking.

Nowadays, camera networks are part of our every-day life environments, consequently, they represent a massive source of information for monitoring human activities and to propose new services to the building users. To perform human activity monitoring, people must be detected and the analysis has to be done according to the information relative to the environment and the context. Available multi-camera datasets furnish videos with few (or none) information of the environment where the network was deployed. The proposed dataset provides multi-camera multi-space video sets along with the complete contextual information of the environment. The dataset regroups 11 video sets (composed of 62 single videos) recorded using 6 indoor cameras deployed on multiple spaces. The video sets represent more than 1 h of video footage, include 77 people tracks and captured different human actions such as walking around, standing/sitting, motionless, entering/leaving a space and group merging/splitting. Moreover, each video has been manually and automatically annotated to include people detection and tracking meta-information. The automatic people detection annotations were obtained by using different complexity and robustness detectors, from machine learning to state-of-art deep Convolutional Neural Network (CNN) models. Concerning the contextual information, the Industry Foundation Classes (IFC) file that represents the environment's Building Information Modeling (BIM) data is also provided. The BIM/IFC file describes the complete structure of the environment, it's topology and the elements contained in it. To our knowledge, the WiseNET dataset is the first to provide a set of videos along with the complete information of the environment. The WiseNET dataset is publicly available at https://doi.org/10.4121/uuid:c1fb5962-e939-4c51-bfd5-eac6f2935d44, as well as at the project's website http://wisenet.checksem.fr/#/dataset.

Augmented Reality of the Middle Ear Combining Otoendoscopy and Temporal Bone Computed Tomography.

HYPOTHESIS: Augmented reality (AR) may enhance otologic procedures by providing sub-millimetric accuracy and allowing the unification of information in a single screen. BACKGROUND: Several issues related to otologic procedures can be addressed through an AR system by providing sub-millimetric precision, supplying a global view of the middle ear cleft, and advantageously unifying the information in a single screen. The AR system is obtained by combining otoendoscopy with temporal bone computer tomography (CT). METHODS: Four human temporal bone specimens were explored by high-resolution CT-scan and dynamic otoendoscopy with video recordings. The initialization of the system consisted of a semi-automatic registration between the otoendoscopic video and the 3D CT-scan reconstruction of the middle ear. Endoscope movements were estimated by several computer vision techniques (feature detectors/descriptors and optical flow) and used to warp the CT-scan to keep the correspondence with the otoendoscopic video. RESULTS: The system maintained synchronization between the CT-scan image and the otoendoscopic video in all experiments during slow and rapid (5-10 mm/s) endoscope movements. Among tested algorithms, two feature-based methods, scale-invariant feature transform (SIFT); and speeded up robust features (SURF), provided sub-millimeter mean tracking errors (0.38 ±â€Š0.53 mm and 0.20 ±â€Š0.16 mm, respectively) and an adequate image refresh rate (11 and 17 frames per second, respectively) after 2 minutes of procedure with continuous endoscope movements. CONCLUSION: A precise augmented reality combining video and 3D CT-scan data can be applied to otoendoscopy without the use of conventional neuronavigation tracking thanks to computer vision algorithms.