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.
Original language | English |
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Article number | 6640 |
Journal | Journal of Clinical Medicine |
Volume | 11 |
Issue number | 22 |
Number of pages | 21 |
ISSN | 2077-0383 |
DOIs | |
Publication status | Published - Nov 2022 |
Keywords
- 3D model
- Cochlea
- Cochlear implant
- Computed tomography
- Deep learning
- Image analysis
- Image segmentation
- Machine learning
- Tonotopic mapping
- Visualization