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Cochlear implantation is currently the most effective treatment for patientswith severe deafness, but mastering cochlear implantation is extremelychallenging because the temporal bone has extremely complex and smallthree-dimensional anatomical structures, and it is important to avoid damagingthe corresponding structures when performing surgery. The spatial location ofthe relevant anatomical tissues within the target area needs to be determinedusing CT prior to the procedure. Considering that the target structures are toosmall and complex, the time required for manual segmentation is too long, andit is extremely challenging to segment the temporal bone and its nearbyanatomical structures quickly and accurately. To overcome this difficulty, wepropose a deep learning-based algorithm, a 3D network model for automaticsegmentation of multi-structural targets in temporal bone CT that canautomatically segment the cochlea, facial nerve, auditory tubercle, vestibuleand semicircular canal. The algorithm combines CNN and Transformer for featureextraction and takes advantage of spatial attention and channel attentionmechanisms to further improve the segmentation effect, the experimental resultscomparing with the results of various existing segmentation algorithms showthat the dice similarity scores, Jaccard coefficients of all targets anatomicalstructures are significantly higher while HD95 and ASSD scores are lower,effectively proving that our method outperforms other advanced methods.

WSC-Trans: A 3D network model for automatic multi-structural segmentation of temporal bone CT