Definition of a coordinate system for multi-modal images of the temporal bone and inner ear.

Purpose: The position and orientation of the head is maintained to be relatively similar during the CT / MR imaging process. However, the position / orientation dissimilarities present in the resulting images between patients, or between different scans of the same patient, do not allow for direct comparison of the images themselves or features / metrics extracted from them. This paper introduces a method of defining a coordinate system which is consistent between patients and modalities (CT and MR) for images of the temporal bone, using easily identifiable landmarks within the semicircular canals.

Developing an Evidence-Based Surgical Curriculum: Learning from a Randomized Controlled Trial of Surgical Rehearsal in Virtual Reality.

Background: Surgical rehearsal - patient-specific preoperative surgical practice - can be provided by virtual reality simulation. This study investigated the effect of surgical rehearsal on cortical mastoidectomy performance and procedure duration.

Methods: University students (n=40) were randomized evenly into a rehearsal and control group.

Automatic analysis of cochlear response using electrocochleography signals during cochlear implant surgery.

Cochlear implants (CIs) provide an opportunity for the hearing impaired to perceive sound through electrical stimulation of the hearing (cochlear) nerve. However, there is a high risk of losing a patient's natural hearing during CI surgery, which has been shown to reduce speech perception in noisy environments as well as music appreciation. This is a major barrier to the adoption of CIs by the hearing impaired.

A Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images.

Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone can provide, integrating such information to be used in segmentation is a challenging task. Numerous methods have been introduced to solve the problem of multi-modal medical image segmentation in recent years.

Automated assessment of cortical mastoidectomy performance in virtual reality.

Introduction: Cortical mastoidectomy is a core skill that Otolaryngology trainees must gain competency in. Automated competency assessments have the potential to reduce assessment subjectivity and bias, as well as reducing the workload for surgical trainers.

Objectives: This study aimed to develop and validate an automated competency assessment system for cortical mastoidectomy.

Surgical approach to the facial recess influences the acceptable trajectory of cochlear implantation electrodes.

Purpose: To provide practical guidance to the operative surgeon by mapping the location, where acceptable straight-line virtual cochlear implant electrode trajectories intersect the facial recess. In addition, to investigate the influence of facial recess preparation, virtual electrode width and surgical approach to the cochlea on these available trajectories.

Methods: The study was performed on imaging data from eight cadaveric temporal bones within the University of Melbourne Virtual Reality (VR) Temporal Bone Surgery Simulator.

A deep learning based framework for the registration of three dimensional multi-modal medical images of the head.

Image registration is a fundamental task in image analysis in which the transform that moves the coordinate system of one image to another is calculated. Registration of multi-modal medical images has important implications for clinical diagnosis, treatment planning, and image-guided surgery as it provides the means of bringing together complimentary information obtained from different image modalities. However, since different image modalities have different properties due to their different acquisition methods, it remains a challenging task to find a fast and accurate match between multi-modal images.

Development of a virtual reality clinically oriented temporal bone anatomy module with randomised control study of three-dimensional display technology.

Objective: To investigate the effectiveness of a virtual reality (VR), three-dimensional (3D) clinically orientated temporal bone anatomy module, including an assessment of different display technologies.

Methods: A clinically orientated, procedural and interactive anatomy module was generated from a micro-CT of a cadaveric temporal bone. The module was given in three different display technologies; 2D, 3D with monoscopic vision, and 3D with stereoscopic vision.

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication.

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task.

The Melbourne Mastoidectomy Scale: Validation of an end-product dissection scale for cortical mastoidectomy.

Introduction: Cortical mastoidectomy is a common otolaryngology procedure and represents a compulsory part of otolaryngology training. As such, a specific validated assessment score is needed for the progression of competency-based training in this procedure. Although multiple temporal bone dissection scales have been developed, they have all been validated for advanced temporal bone dissection including posterior tympanotomy, rather than the task of cortical mastoidectomy.

Street Sign Recognition Using Histogram of Oriented Gradients and Artificial Neural Networks.

Street sign identification is an important problem in applications such as autonomous vehicle navigation and aids for individuals with vision impairments. It can be especially useful in instances where navigation techniques such as global positioning system (GPS) are not available. In this paper, we present a method of detection and interpretation of Malaysian street signs using image processing and machine learning techniques.

A rotation and translation invariant method for 3D organ image classification using deep convolutional neural networks.

Three-dimensional (3D) medical image classification is useful in applications such as disease diagnosis and content-based medical image retrieval. It is a challenging task due to several reasons. First, image intensity values are vastly different depending on the image modality.

Do experts practice what they profess?

We investigated the variation of drilled regions of expert and trainee surgeons performing virtual temporal bone surgery to identify their compliance with standard drilling procedures. To this end, we recruited seven expert and six trainee ENT surgeons, who were asked to perform the surgical preparations for cochlear implantation on a virtual temporal bone. The temporal bone was divided into six regions using a semi-automated approach.

Defining the Hook Region Anatomy of the Guinea Pig Cochlea for Modeling of Inner Ear Surgery.

Hypothesis: The aim of this study was to describe the hook region anatomy of the guinea pig cochlea to identify the optimal surgical approach for cochlear implantation and to determine what anatomical structures are at risk.

Background: Animal studies investigating hearing loss after cochlear implantation surgery are currently constrained by the lack of a reproducible implantation model.

Methods: Guinea pig cochleae were imaged using thin-sheet laser imaging microscopy.

Comparison of Experts and Residents Performing a Complex Procedure in a Temporal Bone Surgery Simulator.

Objective: To investigate the use of automated metrics from a virtual reality (VR) temporal bone surgery simulator to determine how the performance of experts and trainees differs when performing a complex otological procedure (mastoidectomy with posterior tympanotomy and cochleostomy).

Study Design: Cohort study.

Methods: Using the University of Melbourne VR temporal bone surgery simulator, seven ENT consultants and seven ENT residents performed two trials of the surgical approach to cochlear implantation on a virtual temporal bone.

Supporting skill acquisition in cochlear implant surgery through virtual reality simulation.

Objectives: To evaluate the effectiveness of a virtual reality (VR) temporal bone simulator in training cochlear implant surgery.

Methods: We compared the performance of 12 otolaryngology registrars conducting simulated cochlear implant surgery before (pre-test) and after (post-tests) receiving training on a VR temporal bone surgery simulator with automated performance feedback. The post-test tasks were two temporal bones, one that was a mirror image of the temporal bone used as a pre-test and the other, a novel temporal bone.

Correlations of External Landmarks With Internal Structures of the Temporal Bone.

Hypothesis: The internal anatomy of a temporal bone could be inferred from external landmarks.

Background: Mastoid surgery is an important skill that ENT surgeons need to acquire. Surgeons commonly use CT scans as a guide to understanding anatomical variations before surgery.

Developing effective automated feedback in temporal bone surgery simulation.

Objective: We aim to test the effectiveness, accuracy, and usefulness of an automated feedback system in facilitating skill acquisition in virtual reality surgery.

Study Design: We evaluate the performance of the feedback system through a randomized controlled trial of 24 students allocated to feedback and nonfeedback groups.

Setting: The feedback system was based on the Melbourne University temporal bone surgery simulator.

The construct validity and reliability of an assessment tool for competency in cochlear implant surgery.

Introduction: We introduce a rating tool that objectively evaluates the skills of surgical trainees performing cochlear implant surgery.

Methods: Seven residents and seven experts performed cochlear implant surgery sessions from mastoidectomy to cochleostomy on a standardized virtual reality temporal bone. A total of twenty-eight assessment videos were recorded and two consultant otolaryngologists evaluated the performance of each participant using these videos.

A temporal bone surgery simulator with real-time feedback for surgical training.

Timely feedback on surgical technique is an important aspect of surgical skill training in any learning environment, be it virtual or otherwise. Feedback on technique should be provided in real-time to allow trainees to recognize and amend their errors as they occur. Expert surgeons have typically carried out this task, but they have limited time available to spend with trainees.

Constructive real time feedback for a temporal bone simulator.

As demands on surgical training efficiency increase, there is a stronger need for computer assisted surgical training systems. The ability to provide automated performance feedback and assessment is a critical aspect of such systems. The development of feedback and assessment models will allow the use of surgical simulators as self-guided training systems that act like expert trainers and guide trainees towards improved performance.