UMambaAdj：基于UMamba和nnU-Net ResEnc Planner推进MRI引导放疗中头颈癌的GTV分割
UMambaAdj: Advancing GTV Segmentation for Head and Neck Cancer in MRI-Guided RT with UMamba and nnU-Net ResEnc Planner
AI总结：
本研究提出UMambaAdj方法，结合UMamba与nnU-Net ResEnc Planner的优势，在MRI引导放疗中实现更精确的头颈癌GTV分割，测试集平均DSCagg达0.796。
摘要：
磁共振成像（MRI）因其卓越的软组织对比度，在头颈癌（HNC）的MRI引导自适应放疗中发挥着关键作用。然而，准确分割包含原发肿瘤（GTVp）和淋巴结（GTVn）的大体肿瘤体积（GTV）仍具挑战。近期，两项深度学习分割创新展现出巨大前景：能有效捕捉长距离依赖的UMamba，以及通过多阶段残差块增强特征提取的nnU-Net残差编码器（ResEnc）。本研究将这些优势整合为一种名为“UMambaAdj”的新方法。我们使用放疗前T2加权MRI图像，在HNTS-MRG 2024挑战赛测试集上评估了该方法，GTVp的聚合Dice相似系数（DSCagg）为0.751，GTVn为0.842，平均DSCagg为0.796。该方法展示了在MRI引导自适应放疗中更精确勾画肿瘤的潜力，最终改善HNC患者的治疗效果。团队：DCPT-Stine's group。
英文摘要：
Magnetic Resonance Imaging (MRI) plays a crucial role in MRI-guided adaptive radiotherapy for head and neck cancer (HNC) due to its superior soft-tissue contrast. However, accurately segmenting the gross tumor volume (GTV), which includes both the primary tumor (GTVp) and lymph nodes (GTVn), remains challenging. Recently, two deep learning segmentation innovations have shown great promise: UMamba, which effectively captures long-range dependencies, and the nnU-Net Residual Encoder (ResEnc), which enhances feature extraction through multistage residual blocks. In this study, we integrate these strengths into a novel approach, termed 'UMambaAdj'. Our proposed method was evaluated on the HNTS-MRG 2024 challenge test set using pre-RT T2-weighted MRI images, achieving an aggregated Dice Similarity Coefficient (DSCagg) of 0.751 for GTVp and 0.842 for GTVn, with a mean DSCagg of 0.796. This approach demonstrates potential for more precise tumor delineation in MRI-guided adaptive radiotherapy, ultimately improving treatment outcomes for HNC patients. Team: DCPT-Stine's group.
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