Head and neck tumors and metastatic lymph nodes are crucial for treatment planning and prognostic analysis. Accurate segmentation and quantitative analysis of these structures require pixel-level annotation, making automated segmentation techniques essential for the diagnosis and treatment of head and neck cancer. In this study, we investigated the effects of multiple strategies on the segmentation of pre-radiotherapy (pre-RT) and mid-radiotherapy (mid-RT) images. For the segmentation of pre-RT images, we utilized: 1) a fully supervised learning approach, and 2) the same approach enhanced with pre-trained weights and the MixUp data augmentation technique. For mid-RT images, we introduced a novel computational-friendly network architecture that features separate encoders for mid-RT images and registered pre-RT images with their labels. The mid-RT encoder branch integrates information from pre-RT images and labels progressively during the forward propagation. We selected the highest-performing model from each fold and used their predictions to create an ensemble average for inference. In the final test, our models achieved a segmentation performance of 82.38% for pre-RT and 72.53% for mid-RT on aggregated Dice Similarity Coefficient (DSC) as HiLab. Our code is available at https://github.com/WltyBY/HNTS-MRG2024_train_code.

翻译：头颈部肿瘤及转移性淋巴结对治疗计划制定与预后分析至关重要。这些结构的精确分割与定量分析需要像素级标注，这使得自动化分割技术成为头颈部癌症诊疗的关键。本研究探讨了多种策略对放疗前（pre-RT）与放疗中（mid-RT）图像分割的影响。针对放疗前图像分割，我们采用：1）全监督学习方法，以及2）结合预训练权重与MixUp数据增强技术的改进方法。对于放疗中图像，我们提出了一种新颖的计算友好型网络架构，该架构配备独立编码器分别处理放疗中图像，以及已配准的放疗前图像及其标签。放疗中编码器分支在前向传播过程中逐步整合来自放疗前图像与标签的信息。我们从每个交叉验证折中选取性能最优的模型，并利用其预测结果进行集成平均推理。在最终测试中，我们的模型在HiLab的聚合Dice相似系数（DSC）指标上，分别实现了放疗前82.38%与放疗中72.53%的分割性能。代码已发布于https://github.com/WltyBY/HNTS-MRG2024_train_code。