Evaluating and Improving Cochlear Length Measurements on Clinical Computed Tomography Images

Cochlear implants provide the sensation of sound to deaf individuals. An accurate estimate of cochlear duct length (CDL) is required for pre-operative implant electrode selection and can be obtained from clinical computed tomography (CT) by measuring the “A-value”. The objectives of this work were to estimate the accuracy and variability in manual A-value measurements, and to automate measurements. Four specialists repeatedly measured the A-value on clinical CT images from which the inter- and intra-observer variability were calculated. Accuracy was assessed by comparison to measurements on higher resolution micro-CT images. Motivated by this study, software was developed to automate the A-value measurement by registering an annotated atlas to unlabelled images. There was significant variability in manual A-value measurements made using either standard clinical or multi-planar reformatted views with the latter exhibiting higher variability but better accuracy. The automated approach eliminated variability and improved accuracy, enabling the correct selection of electrode length

A Web-Based Automated Image Processing Research Platform for Cochlear Implantation-Related Studies

International audienceThe robust delineation of the cochlea and its inner structures combined with the detection of the electrode of a cochlear implant within these structures is essential for envisaging a safer, more individualized, routine image-guided cochlear implant therapy. We present Nautilus—a web-based research platform for automated pre- and post-implantation cochlear analysis. Nautilus delineates cochlear structures from pre-operative clinical CT images by combining deep learning and Bayesian inference approaches. It enables the extraction of electrode locations from a post-operative CT image using convolutional neural networks and geometrical inference. By fusing pre- and post-operative images, Nautilus is able to provide a set of personalized pre- and post-operative metrics that can serve the exploration of clinically relevant questions in cochlear implantation therapy. In addition, Nautilus embeds a self-assessment module providing a confidence rating on the outputs of its pipeline. We present a detailed accuracy and robustness analyses of the tool on a carefully designed dataset. The results of these analyses provide legitimate grounds for envisaging the implementation of image-guided cochlear implant practices into routine clinical workflows

Self-supervised Registration and Segmentation of the Ossicles with A Single Ground Truth Label

AI-assisted surgeries have drawn the attention of the medical image research community due to their real-world impact on improving surgery success rates. For image-guided surgeries, such as Cochlear Implants (CIs), accurate object segmentation can provide useful information for surgeons before an operation. Recently published image segmentation methods that leverage machine learning usually rely on a large number of manually predefined ground truth labels. However, it is a laborious and time-consuming task to prepare the dataset. This paper presents a novel technique using a self-supervised 3D-UNet that produces a dense deformation field between an atlas and a target image that can be used for atlas-based segmentation of the ossicles. Our results show that our method outperforms traditional image segmentation methods and generates a more accurate boundary around the ossicles based on Dice similarity coefficient and point-to-point error comparison. The mean Dice coefficient is improved by 8.51% with our proposed method.Comment: conferenc

Accuracy of image guided robotic assistance in cochlear implant surgery

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