融合UMamba与nnU-Net残差编码器，提升头颈癌MRI肿瘤分割精度。
UMambaAdj: Advancing GTV Segmentation for Head and Neck Cancer in MRI-Guided RT with UMamba and nnU-Net ResEnc Planner
- 结合UMamba长程建模与nnU-Net多阶段残差块，增强特征表达。
- GTVp和GTVn的平均骰子系数达0.796，分别达到0.751和0.842。
- 适合需高精度肿瘤勾画的放疗影像分析场景。
磁共振成像（MRI）在头颈癌（HNC）的磁共振引导自适应放疗中至关重要，因其优异的软组织对比度。然而，准确分割包括原发灶（GTVp）和淋巴结（GTVn）的总肿瘤体积（GTV）仍具挑战。近期，两种深度学习分割创新展现出巨大潜力：UMamba可有效捕捉长距离依赖，nnU-Net残差编码器（ResEnc）通过多阶段残差块提升特征提取能力。本研究将二者优势整合，提出新方法「UMambaAdj」。在HNTS-MRG 2024挑战赛测试集上，基于放疗前T2加权MRI图像评估，GTVp的聚合骰子系数（DSCagg）为0.751，GTVn为0.842，平均DSCagg为0.796。该方法展现了在磁共振引导自适应放疗中实现更精准肿瘤勾画的潜力，有望改善头颈癌患者治疗效果。团队：DCPT-Stine's group。
原文摘要 · Abstract (English)
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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