  Background and objective: Parotid gland tumors account for approximately 2%
to 10% of head and neck tumors. Preoperative tumor localization, differential
diagnosis, and subsequent selection of appropriate treatment for parotid gland
tumors are critical. However, the relative rarity of these tumors and the
highly dispersed tissue types have left an unmet need for a subtle differential
diagnosis of such neoplastic lesions based on preoperative radiomics. Recently,
deep learning methods have developed rapidly, especially Transformer beats the
traditional convolutional neural network in computer vision. Many new
Transformer-based networks have been proposed for computer vision tasks.
Methods: In this study, multicenter multimodal parotid gland MR images were
collected. The Swin-Unet which was based on Transformer was used. MR images of
short time inversion recovery, T1-weighted and T2-weighted modalities were
combined into three-channel data to train the network. We achieved segmentation
of the region of interest for parotid gland and tumor. Results: The
Dice-Similarity Coefficient of the model on the test set was 88.63%, Mean Pixel
Accuracy was 99.31%, Mean Intersection over Union was 83.99%, and Hausdorff
Distance was 3.04. Then a series of comparison experiments were designed in
this paper to further validate the segmentation performance of the algorithm.
Conclusions: Experimental results showed that our method has good results for
parotid gland and tumor segmentation. The Transformer-based network outperforms
the traditional convolutional neural network in the field of medical images.
