基于预训练、数据增强和双流UNet的放疗前与放疗中MRI头颈部肿瘤分割
Head and Neck Tumor Segmentation of MRI from Pre- and Mid-radiotherapy with Pre-training, Data Augmentation and Dual Flow UNet
AI总结：
本研究针对放疗前与放疗中MRI头颈部肿瘤分割问题，提出结合预训练、MixUp数据增强及双流UNet架构的方法，最终在测试集上取得放疗前82.38%、放疗中72.53%的DSC分割性能。
摘要：
头颈部肿瘤和转移性淋巴结对治疗计划和预后分析至关重要。这些结构的准确分割和定量分析需要像素级标注，使得自动分割技术对头颈部癌的诊断和治疗不可或缺。本研究探讨了多种策略对放疗前和放疗中图像分割的影响。对于放疗前图像的分割，我们采用了：1）全监督学习方法，2）结合预训练权重和MixUp数据增强技术的改进方法。对于放疗中图像，我们引入了一种新型计算友好型网络架构，为放疗中图像和配准的放疗前图像及其标签设置独立编码器。放疗中编码器分支在前向传播过程中逐步整合放疗前图像和标签的信息。我们从每折中选择表现最佳的模型，并使用其预测结果创建集成平均进行推理。在最终测试中，我们的模型作为HiLab团队，在聚合Dice相似系数（DSC）上取得了放疗前82.38%和放疗中72.53%的分割性能。代码可在https://github.com/WltyBY/HNTS-MRG2024_train_code获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
