Automated Performance Assessment of Virtual Temporal Bone Dissection
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2020-07-21
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Abstract
Mastoidectomy is a surgical procedure in which a portion of the temporal bone is removed by using fine microsurgical skills. Development of virtual reality simulators with high-fidelity visual, auditory, and force feedback has allowed trainees to learn this skill in a safe environment without the limitations associated with the traditional way of learning, i.e., cadaveric specimens. However, without an automatic feedback mechanism, an expert's presence is required to assess the performance, placing a heavy burden on their time. This investigation focuses on automating the performance evaluation obviating the need for an expert's time. This is accomplished by automating the criteria based on a well-established and validated assessment instrument known as the Welling Scale, to score the mastoidectomy performed on a virtual surgery simulator. Image processing algorithms are devised and run on the output of the virtual surgery to automatically score these criteria. The criteria are described in terms of four functional categories: Identification, Skeletonization, Intactness and No cells. Algorithms are devised for each of these categories. This work further validates the accuracy of these algorithms by doing a study where these criteria are evaluated by two experts, as well as the work done in this thesis. The results of the study show that automatic performance assessment of virtual mastoidectomy surgery is feasible.
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Sachan, S. (2020). Automated Performance Assessment of Virtual Temporal Bone Dissection (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.