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

**Jan Margeta 1,\*, Raabid Hussain 2, Paula López Diez 3, Anika Morgenstern 4, Thomas Demarcy 2, Zihao Wang 5, Dan Gnansia 2, Octavio Martinez Manzanera 2, Clair Vandersteen 6, Hervé Delingette 5, Andreas Buechner 4, Thomas Lenarz 4, François Patou <sup>2</sup> and Nicolas Guevara <sup>6</sup>**


**Abstract:** 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.

**Keywords:** cochlea; cochlear implant; image analysis; computed tomography; machine learning; deep learning; image segmentation; 3D model; tonotopic mapping; visualization
