Tie Xin, Chen Weijie, Huemann Zachary, Schott Brayden, Liu Nuohao, Bradshaw Tyler J
University of Wisconsin, Madison, WI, USA.
Head Neck Tumor Segm MR Guid Appl (2024). 2025;15273:99-111. doi: 10.1007/978-3-031-83274-1_7. Epub 2025 Mar 3.
Accurate segmentation of gross tumor volume (GTV) is essential for effective MRI-guided adaptive radiotherapy (MRgART) in head and neck cancer. However, manual segmentation of the GTV over the course of therapy is time-consuming and prone to interobserver variability. Deep learning (DL) has the potential to overcome these challenges by automatically delineating GTVs. In this study, our team, , tackled the challenges of both pre-radiotherapy (pre-RT) (Task 1) and mid-radiotherapy (mid-RT) (Task 2) tumor volume segmentation. To this end, we developed a series of DL models for longitudinal GTV segmentation. The backbone of our models for both tasks was SegResNet with deep supervision. For Task 1, we trained the model using a combined dataset of pre-RT and mid-RT MRI data, which resulted in the improved aggregated Dice similarity coefficient (DSC) on a hold-out internal testing set compared to models trained solely on pre-RT MRI data. In Task 2, we introduced mask-aware attention modules, enabling pre-RT GTV masks to influence intermediate features learned from mid-RT data. This attention-based approach yielded slight improvements over the baseline method, which concatenated mid-RT MRI with pre-RT GTV masks as input. In the final testing phase, the ensemble of 10 pre-RT segmentation models achieved an average DSC of 0.794, with 0.745 for primary GTV (GTVp) and 0.844 for metastatic lymph nodes (GTVn) in Task 1. For Task 2, the ensemble of 10 mid-RT segmentation models attained an average DSC of 0.733, with 0.607 for GTVp and 0.859 for GTVn, leading us to achieve 1st place. In summary, we presented a collection of DL models that could facilitate GTV segmentation in MRgART, offering the potential to streamline radiation oncology workflows.
精确分割大体肿瘤体积（GTV）对于头颈部癌的有效磁共振成像引导自适应放疗（MRgART）至关重要。然而，在整个治疗过程中手动分割GTV既耗时又容易出现观察者间差异。深度学习（DL）有潜力通过自动勾勒GTV来克服这些挑战。在本研究中，我们的团队应对了放疗前（pre-RT）（任务1）和放疗中期（mid-RT）（任务2）肿瘤体积分割的挑战。为此，我们开发了一系列用于纵向GTV分割的DL模型。我们这两个任务模型的主干都是带有深度监督的SegResNet。对于任务1，我们使用放疗前和放疗中期MRI数据的组合数据集训练模型，与仅使用放疗前MRI数据训练的模型相比，在保留的内部测试集上提高了聚合骰子相似系数（DSC）。在任务2中，我们引入了掩码感知注意力模块，使放疗前GTV掩码能够影响从放疗中期数据中学习到的中间特征。这种基于注意力的方法比将放疗中期MRI与放疗前GTV掩码连接作为输入的基线方法略有改进。在最终测试阶段，10个放疗前分割模型的集成在任务1中实现了平均DSC为0.794，原发性GTV（GTVp）为0.745，转移性淋巴结（GTVn）为0.844。对于任务2，10个放疗中期分割模型的集成实现了平均DSC为0.733，GTVp为0.607，GTVn为0.859，使我们获得了第一名。总之，我们展示了一组DL模型，它们可以促进MRgART中的GTV分割，为简化放射肿瘤学工作流程提供了潜力。
