Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images

• URL: http://arxiv.org/abs/2206.03336v1
• Date: Tue, 7 Jun 2022 14:20:53 GMT
• Title: Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images
• Authors: Yin Dai, Zi'an Xu, Fayu Liu, Siqi Li, Sheng Liu, Lifu Shi, Jun Fu
• Abstract summary: Parotid gland tumors account for approximately 2% to 10% of head and neck tumors. Deep learning methods have developed rapidly, especially Transformer beats the traditional convolutional neural network in computer vision. The DSC of the model on the test set was 88.63%, MPA was 99.31%, MIoU was 83.99%, and HD was 3.04.
• Score: 7.934520786027202
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 is 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. In this study, multicenter multimodal parotid gland MRI images were collected. The Swin-Unet which was based on Transformer was used. MRI images of STIR, T1 and T2 modalities were combined into a three-channel data to train the network. We achieved segmentation of the region of interest for parotid gland and tumor. The DSC of the model on the test set was 88.63%, MPA was 99.31%, MIoU was 83.99%, and HD was 3.04. Then a series of comparison experiments were designed in this paper to further validate the segmentation performance of the algorithm.

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