Journal
JOURNAL OF CLINICAL MEDICINE
Volume 11, Issue 22, Pages -Publisher
MDPI
DOI: 10.3390/jcm11226640
Keywords
cochlea; cochlear implant; image analysis; computed tomography; machine learning; deep learning; image segmentation; 3D model; tonotopic mapping; visualization
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This study presents Nautilus, a web-based platform for automated cochlear analysis. It combines deep learning and Bayesian inference methods to delineate cochlear structures from clinical CT images and extract electrode locations from post-operative images. By fusing pre- and post-operative images, Nautilus can provide personalized metrics for research in cochlear implantation therapy.
The 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.
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