Abstract
We propose a pipeline for the characterization of facial and cochlear nerves in CT scans, a task specifically relevant for cochlear implant surgery planning. These structures are hard to locate in clinical CT scans due to their small size relative to the image resolution, the lack of contrast, and the proximity to other similar structures in this region. We define key landmarks around the facial and cochlear nerves and locate them using deep reinforcement learning with communicative multi-agents based on the C-MARL model. These landmarks are used as initialization for customized characterization methods. These include the automated direct measurement of the diameter of the cochlear nerve canal and extraction of the cochlear nerve cross-section followed by its segmentation using active contours. We also derive a path selection algorithm for optimal geodesic pathfinding selection based on Dijkstra’s algorithm for the characterization of the facial nerve. A total of 119 clinical CT images from preoperative patients have been used to develop this pipeline that produces accurate characterizations of these nerves in the cochlear region and provides reliable measurements for computer-aided diagnosis and surgery planning.
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López Diez, P., Sundgaard, J.V., Patou, F., Margeta, J., Paulsen, R.R. (2021). Facial and Cochlear Nerves Characterization Using Deep Reinforcement Learning for Landmark Detection. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12904. Springer, Cham. https://doi.org/10.1007/978-3-030-87202-1_50
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