A Review of Physical Simulators for Neuroendoscopy Skills Training.

BACKGROUND: Minimally invasive neurosurgical approaches reduce patient morbidity by providing the surgeon with better visualization and access to complex lesions, with minimal disruption to normal anatomy. The use of rigid or flexible neuroendoscopes, supplemented with a conventional stereoscopic operating microscope, has been integral to the adoption of these techniques. Neurosurgeons commonly use neuroendoscopes to perform the ventricular and endonasal approaches. It is challenging to learn neuroendoscopy skills from the existing apprenticeship model of surgical education. The training methods, which use simulation-based systems, have achieved wide acceptance. Physical simulators provide anatomic orientation and hands-on experience with repeatability. Our aim is to review the existing physical simulators on the basis of the skills training of neuroendoscopic procedures. METHODS: We searched Scopus, Google Scholar, PubMed, IEEE Xplore, and dblp. We used the following keywords "neuroendoscopy," "training," "simulators," "physical," and "skills evaluation." A total of 351 articles were screened based on development methods, evaluation criteria, and validation studies on physical simulators for skills training in neuroendoscopy. RESULTS: The screening of the articles resulted in classifying the physical training methods developed for neuroendoscopy surgical skills into synthetic simulators and box trainers. The existing simulators were compared based on their design, fidelity, trainee evaluation methods, and validation studies. CONCLUSIONS: The state of simulation systems demands collaborative initiatives among translational research institutes. They need improved fidelity and validation studies for inclusion in the surgical educational curriculum. Learning should be imparted in stages with standardization of performance metrics for skills evaluation.

A review of virtual reality simulators for neuroendoscopy.

Neurosurgery is a challenging surgical specialty that demands many technical and cognitive skills. The traditional surgical training approach of having a trainee coached in the operating room by the faculty is time-consuming, costly, and involves patient risk factors. Simulation-based training methods are suitable to impart the surgical skills outside the operating room. Virtual simulators allow high-fidelity repeatable environment for surgical training. Neuroendoscopy, a minimally invasive neurosurgical technique, demands additional skills for limited maneuverability and eye-hand coordination. This study provides a review of the existing virtual reality simulators for training neuroendoscopic skills. Based on the screening, the virtual training methods developed for neuroendoscopy surgical skills were classified into endoscopic third ventriculostomy and endonasal transsphenoidal surgery trainers. The study revealed that a variety of virtual reality simulators have been developed by various institutions. Although virtual reality simulators are effective for procedure-based skills training, the simulators need to include anatomical variations and variety of cases for improved fidelity. The review reveals that there should be multi-centric prospective and retrospective cohort studies to establish concurrent and predictive validation for their incorporation in the surgical educational curriculum.

Generalized Flows for Optimal Inference in Higher Order MRF-MAP.

Use of higher order clique potentials in MRF-MAP problems has been limited primarily because of the inefficiencies of the existing algorithmic schemes. We propose a new combinatorial algorithm for computing optimal solutions to 2 label MRF-MAP problems with higher order clique potentials. The algorithm runs in time O(2(k)n(3)) in the worst case (k is size of clique and n is the number of pixels). A special gadget is introduced to model flows in a higher order clique and a technique for building a flow graph is specified. Based on the primal dual structure of the optimization problem, the notions of the capacity of an edge and a cut are generalized to define a flow problem. We show that in this flow graph, when the clique potentials are submodular, the max flow is equal to the min cut, which also is the optimal solution to the problem. We show experimentally that our algorithm provides significantly better solutions in practice and is hundreds of times faster than solution schemes like Dual Decomposition [1], TRWS [2] and Reduction [3], [4], [5]. The framework represents a significant advance in handling higher order problems making optimal inference practical for medium sized cliques.

On efficient learning machine with root-power mean neuron in complex domain.

This paper describes an artificial neuron structure and an efficient learning procedure in the complex domain. This artificial neuron aims at incorporating an improved aggregation operation on the complex-valued signals. The aggregation operation is based on the idea underlying the weighted root-power mean of input signals. This aggregation operation allows modeling the degree of compensation in a natural manner and includes various aggregation operations as its special cases. The complex resilient propagation algorithm ([Formula: see text]-RPROP) with error-dependent weight backtracking step accelerates the training speed significantly and provides better approximation accuracy. Finally, performance evaluation of the proposed complex root-power mean neuron with the [Formula: see text]-RPROP learning algorithm on various typical examples is given to understand the motivation.

Space-time super-resolution using graph-cut optimization.

We address the problem of super-resolution­obtaining high-resolution images and videos from multiple low-resolution inputs. The increased resolution can be in spatial or temporal dimensions, or even in both. We present a unified framework which uses a generative model of the imaging process and can address spatial super-resolution, space-time super-resolution, image deconvolution, single-image expansion, removal of noise, and image restoration. We model a high-resolution image or video as a Markov random field and use maximum a posteriori estimate as the final solution using graph-cut optimization technique. We derive insights into what super-resolution magnification factors are possible and the conditions necessary for super-resolution. We demonstrate spatial super-resolution reconstruction results with magnifications higher than predicted limits of magnification. We also formulate a scheme for selective super-resolution reconstruction of videos to obtain simultaneous increase of resolutions in both spatial and temporal directions. We show that it is possible to achieve space-time magnification factors beyond what has been suggested in the literature by selectively applying super-resolution constraints. We present results on both synthetic and real input sequences.