ArXiv Preprint
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.
Xin Hua, Zhijiang Du, Hongjian Yu, Jixin Ma, Fanjun Zheng, Cheng Zhang, Qiaohui Lu, Hui Zhao
2022-11-14
