Nasopharyngeal carcinoma is a malignant tumor that occurs in the epithelium and mucosal glands of the nasopharynx, and its pathological type is mostly poorly differentiated squamous cell carcinoma. Since the nasopharynx is located deep in the head and neck, early diagnosis and timely treatment are critical to patient survival. However, nasopharyngeal carcinoma tumors are small in size and vary widely in shape, and it is also a challenge for experienced doctors to delineate tumor contours. In addition, due to the special location of nasopharyngeal carcinoma, complex treatments such as radiotherapy or surgical resection are often required, so accurate pathological diagnosis is also very important for the selection of treatment options. However, the current deep learning segmentation model faces the problems of inaccurate segmentation and unstable segmentation process, which are mainly limited by the accuracy of data sets, fuzzy boundaries, and complex lines. In order to solve these two challenges, this article proposes a hybrid model WET-UNet based on the UNet network as a powerful alternative for nasopharyngeal cancer image segmentation. On the one hand, wavelet transform is integrated into UNet to enhance the lesion boundary information by using low-frequency components to adjust the encoder at low frequencies and optimize the subsequent computational process of the Transformer to improve the accuracy and robustness of image segmentation. On the other hand, the attention mechanism retains the most valuable pixels in the image for us, captures the remote dependencies, and enables the network to learn more representative features to improve the recognition ability of the model. Comparative experiments show that our network structure outperforms other models for nasopharyngeal cancer image segmentation, and we demonstrate the effectiveness of adding two modules to help tumor segmentation. The total data set of this article is 5000, and the ratio of training and verification is 8:2. In the experiment, accuracy = 85.2% and precision = 84.9% can show that our proposed model has good performance in nasopharyngeal cancer image segmentation.

中文翻译：

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WET-UNet：小波集成高效变压器网络用于鼻咽癌肿瘤分割

鼻咽癌是发生于鼻咽部上皮和粘膜腺的恶性肿瘤，其病理类型多为低分化鳞状细胞癌。由于鼻咽部位于头颈部深处，早期诊断和及时治疗对于患者的生存至关重要。然而，鼻咽癌肿瘤体积较小，形状差异很大，对有经验的医生勾画肿瘤轮廓也是一个挑战。此外，由于鼻咽癌的特殊部位，往往需要进行放射治疗或手术切除等复杂的治疗，因此准确的病理诊断对于治疗方案的选择也非常重要。然而，当前的深度学习分割模型面临分割不准确和分割过程不稳定的问题，这主要受到数据集精度、边界模糊和复杂线条的限制。为了解决这两个挑战，本文提出了一种基于UNet网络的混合模型WET-UNet，作为鼻咽癌图像分割的有力替代方案。一方面，将小波变换集成到UNet中，利用低频分量来增强病灶边界信息，从而在低频处调整编码器并优化Transformer的后续计算过程，以提高图像分割的准确性和鲁棒性。另一方面，注意力机制为我们保留了图像中最有价值的像素，捕获远程依赖关系，使网络能够学习更多有代表性的特征，以提高模型的识别能力。对比实验表明，我们的网络结构在鼻咽癌图像分割方面优于其他模型，并且我们证明了添加两个模块来帮助肿瘤分割的有效性。本文总数据集为5000，训练与验证比例为8:2。在实验中，准确率= 85.2％，精度= 84.9％，可以表明我们提出的模型在鼻咽癌图像分割方面具有良好的性能。