Background and purpose: Deep Learning (DL) has been widely explored for Organs at Risk (OARs) segmentation; however, most studies have focused on a single modality, either CT or MRI, not both simultaneously. This study presents a high-performing DL pipeline for segmentation of 30 OARs from MRI and CT scans of Head and Neck (H&N) cancer patients. Materials and methods: Paired CT and MRI-T1 images from 42 H&N cancer patients alongside annotation for 30 OARs from the H&N OAR CT & MR segmentation challenge dataset were used to develop a segmentation pipeline. After cropping irrelevant regions, rigid followed by non-rigid registration of CT and MRI volumes was performed. Two versions of the CT volume, representing soft tissues and bone anatomy, were stacked with the MRI volume and used as input to an nnU-Net pipeline. Modality Dropout was used during the training to force the model to learn from the different modalities. Segmentation masks were predicted with the trained model for an independent set of 14 new patients. The mean Dice Score (DS) and Hausdorff Distance (HD) were calculated for each OAR across these patients to evaluate the pipeline. Results: This resulted in an overall mean DS and HD of 0.777 +- 0.118 and 3.455 +- 1.679, respectively, establishing the state-of-the-art (SOTA) for this challenge at the time of submission. Conclusion: The proposed pipeline achieved the best DS and HD among all participants of the H&N OAR CT and MR segmentation challenge and sets a new SOTA for automated segmentation of H&N OARs.

翻译：背景与目的：深度学习在危险器官分割中已被广泛探索，但大多数研究聚焦于单一模态（CT或MRI），而非二者同步应用。本研究提出一种高性能深度学习流程，用于从头颈癌患者的MRI与CT扫描中分割30个危险器官。材料与方法：采用H&N OAR CT与MR分割挑战数据集中42例头颈癌患者的配对CT与MRI-T1图像及对应30个危险器官的标注，开发分割流程。在对无关区域进行裁剪后，依次实施刚性配准与非刚性配准以对齐CT与MRI容积。将代表软组织与骨骼解剖结构的两种CT容积版本与MRI容积堆叠后输入nnU-Net流程。训练期间引入模态丢弃策略，强制模型从不同模态中学习。利用训练模型对独立测试集（14例新患者）预测分割掩膜。计算每例患者各危险器官的平均Dice系数与豪斯多夫距离以评估流程性能。结果：整体平均Dice系数与豪斯多夫距离分别为0.777±0.118与3.455±1.679，在提交时确立该挑战的当前最优性能。结论：所提流程在H&N OAR CT与MR分割挑战的所有参赛者中取得最优Dice系数与豪斯多夫距离，为头颈癌危险器官自动分割树立了新的当前最优性能基准。