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Image-guided neurosurgery, or neuronavigation, has been used to visualise the location of a surgical probe by mapping the probe location to pre-operative models of a patient's anatomy. One common limitation of this approach is that it requires the surgeon to divert their attention away from the patient and towards the neuronavigation system. In order to improve this type of application, the authors designed a system that sonifies (i.e. provides audible feedback of) distance information between a surgical probe and the location of the anatomy of interest. A user study (n = 15) was completed to determine the utility of sonified distance information within an existing neuronavigation platform (Intraoperative Brain Imaging System (IBIS) Neuronav). The authors’ results were consistent with the idea that combining auditory distance cues with existing visual information from image-guided surgery systems may result in greater accuracy when locating specified points on a pre-operative scan, thereby potentially reducing the extent of the required surgical openings, as well as potentially increasing the precision of individual surgical tasks. Further, the authors’ results were also consistent with the hypothesis that combining auditory and visual information reduces the perceived difficulty in locating a target location within a three-dimensional volume.

Minimally invasive techniques, such as laparoscopy and radiofrequency ablation of tumors, bring important advantages in surgery: by minimizing incisions on the patient's body, they can reduce the hospitalization period and the risk of postoperative complications. Unfortunately, they come with drawbacks for surgeons, who have a restricted vision of the operation area through an indirect access and 2D images provided by a camera inserted in the body. Augmented reality provides an "X-ray vision" of the patient anatomy thanks to the visualization of the internal organs of the patient. In this way, surgeons are free from the task of mentally associating the content from CT images to the operative scene. We present a navigation system that supports surgeons in preoperative and intraoperative phases and an augmented reality system that superimposes virtual organs on the patient's body together with depth and distance information. We implemented a combination of visual and audio cues allowing the surgeon to improve the intervention precision and avoid the risk of damaging anatomical structures. The test scenarios proved the good efficacy and accuracy of the system. Moreover, tests in the operating room suggested some modifications to the tracking system to make it more robust with respect to occlusions. Graphical Abstract Augmented visualization in minimally invasive surgery.

The distance labeled curve skeleton of a 3D object is used to guide the decomposition of the object into disjoint parts. To this aim, the skeleton is polygonally approximated by taking into account spatial coordinates and distance values of its voxels. The obtained skeleton segments are characterized by the absence of significant changes of curvature. Moreover, along each skeleton segment distance values are constant or show a linearly changing trend. Each segment can be interpreted as the spine of a simple part, which is characterized by the absence of significant curvature changes along its boundary, and by local thickness that is constant or evolves linearly along the part. By using exclusively the spatial coordinates and distance values of the vertices of the spines, quantitative information on shape, size, position and orientation of the corresponding simple parts can be obtained. Alternative decompositions of the object can be computed by selecting different values for the threshold used during polygonal approximation of the skeleton. Some criteria are also discussed for the selection of optimal threshold values originating approximated skeletons having a small number of segments, but still producing decompositions rather well adapting the input object boundary.

Discrete method for 3D object decomposition into perceptually simple parts.Use of the curve skeleton to guide object decomposition.Polygonal approximation of skeleton branches to obtain spines of simple regions. A method to decompose a 3D object into simple parts starting from its curve skeleton is described. Branches of the curve skeleton are classified as meaningful and non-meaningful by using the notion of zone of influence of the points where skeleton branches meet. Meaningful branches are associated to subsets of the object, which are obtained by subtracting from the input object suitably expanded versions of the zones of influence, termed overlapping regions. Decision is taken on whether the overlapping regions should be individual decomposition components, or should be assigned to properly selected adjacent object's subsets. The so obtained object's decomposition components are subdivided into parts characterized by simple shape through the polygonal approximation of the corresponding skeleton branches.

Partially supported by TUBITAK grant 106E071. We construct a robust localization framework to handle noisy measurements in wireless sensor networks. Traditionally many approaches employ the distance information gathered from ranging devices of the sensor nodes to achieve localization. However the measurements of these devices may contain noise both as hardware noise and as environmental noise due to the employment conditions of the network. It Is necessary to provide a general framework that handles such a noise in data and yet still be applicable within several localization algorithms. In order to handle noise in distance measurements, our framework utilizes convex constraints and confidence intervals of a random variable. At the end of the localization process nodes are assigned to a set of feasible regions with corresponding probabilities. The accuracy of the localization can be adjusted and the framework can easily be embedded to work within previously suggested localization algorithms. Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) Publisher's Version WOS:000275024200125