Wodzinski Marek
Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland.
Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais-Wallis), Sierre, Switzerland.
Head Neck Tumor Segm MR Guid Appl (2024). 2025;15273:204-213. doi: 10.1007/978-3-031-83274-1_15. Epub 2025 Mar 3.
Radiation therapy is one of the most frequently applied cancer treatments worldwide, especially in the context of head and neck cancer. Today, MRI-guided radiation therapy planning is becoming increasingly popular due to good soft tissue contrast, lack of radiation dose delivered to the patient, and the capability of performing functional imaging. However, MRI-guided radiation therapy requires segmenting of the cancer both before and during radiation therapy. So far, the segmentation was often performed manually by experienced radiologists, however, recent advances in deep learning-based segmentation suggest that it may be possible to perform the segmentation automatically. Nevertheless, the task is arguably more difficult when using MRI compared to e.g. PET-CT because even manual segmentation of head and neck cancer in MRI volumes is challenging and time-consuming. The importance of the problem motivated the researchers to organize the HNTSMRG challenge with the aim of developing the most accurate segmentation methods, both before and during MRI-guided radiation therapy. In this work, we benchmark several different state-of-the-art segmentation architectures to verify whether the recent advances in deep encoder-decoder architectures are impactful for low data regimes and low-contrast tasks like segmenting head and neck cancer in magnetic resonance images. We show that for such cases the traditional residual UNetbased method outperforms (DSC = 0.775/0.701) recent advances such as UNETR (DSC = .617/0.657), SwinUNETR (DSC = 0.757/0.700), or SegMamba (DSC = 0.708/0.683). The proposed method (lWM team) achieved a mean aggregated Dice score on the closed test set at the level of 0.771 and 0.707 for the pre- and mid-therapy segmentation tasks, scoring 14th and 6th place, respectively. The results suggest that proper data preparation, objective function, and preprocessing are more influential for the segmentation of head and neck cancer than deep network architecture.
放射治疗是全球最常用的癌症治疗方法之一，尤其是在头颈癌的治疗中。如今，由于良好的软组织对比度、对患者的辐射剂量低以及进行功能成像的能力，磁共振成像（MRI）引导的放射治疗计划越来越受欢迎。然而，MRI引导的放射治疗在放疗前和放疗期间都需要对癌症进行分割。到目前为止，分割通常由经验丰富的放射科医生手动完成，不过，基于深度学习的分割技术的最新进展表明，自动分割是有可能实现的。然而，与例如正电子发射断层扫描-计算机断层扫描（PET-CT）相比，使用MRI时这项任务可能更困难，因为即使是对MRI图像中的头颈癌进行手动分割也具有挑战性且耗时。该问题的重要性促使研究人员组织了头颈肿瘤MRI图像分割挑战赛（HNTSMRG），旨在开发在MRI引导的放射治疗前和放疗期间最准确的分割方法。在这项工作中，我们对几种不同的先进分割架构进行了基准测试，以验证深度编码器-解码器架构的最新进展对于低数据量情况和低对比度任务（如在磁共振图像中分割头颈癌）是否有影响。我们表明，对于此类情况，传统的基于残差UNet的方法优于（Dice相似系数 = 0.775/0.701）诸如UNETR（Dice相似系数 = 0.617/0.657）、SwinUNETR（Dice相似系数 = 0.757/0.700）或SegMamba（Dice相似系数 = 0.708/0.683）等最新进展。所提出的方法（lWM团队）在封闭测试集上，对于治疗前和治疗中期的分割任务，平均聚合Dice分数分别为0.771和0.707，分别排名第14和第6。结果表明，对头颈癌分割而言，适当的数据准备、目标函数和预处理比深度网络架构更具影响力。
