WSC-Trans: A 3D network model for automatic multi-structural segmentation of temporal bone CT
This addresses the challenge of time-consuming manual segmentation for surgeons planning cochlear implant surgery, though it appears incremental as it combines existing CNN and Transformer approaches.
The paper tackles the problem of automatically segmenting complex anatomical structures in temporal bone CT scans for cochlear implantation planning, achieving significantly higher dice similarity scores and Jaccard coefficients while reducing HD95 and ASSD scores compared to existing methods.
Cochlear implantation is currently the most effective treatment for patients with severe deafness, but mastering cochlear implantation is extremely challenging because the temporal bone has extremely complex and small three-dimensional anatomical structures, and it is important to avoid damaging the corresponding structures when performing surgery. The spatial location of the relevant anatomical tissues within the target area needs to be determined using CT prior to the procedure. Considering that the target structures are too small and complex, the time required for manual segmentation is too long, and it is extremely challenging to segment the temporal bone and its nearby anatomical structures quickly and accurately. To overcome this difficulty, we propose a deep learning-based algorithm, a 3D network model for automatic segmentation of multi-structural targets in temporal bone CT that can automatically segment the cochlea, facial nerve, auditory tubercle, vestibule and semicircular canal. The algorithm combines CNN and Transformer for feature extraction and takes advantage of spatial attention and channel attention mechanisms to further improve the segmentation effect, the experimental results comparing with the results of various existing segmentation algorithms show that the dice similarity scores, Jaccard coefficients of all targets anatomical structures are significantly higher while HD95 and ASSD scores are lower, effectively proving that our method outperforms other advanced methods.